Bibliografias temáticas / Histopathologie digitale

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Literatura científica selecionada sobre o tema "Histopathologie digitale"

Autor: Grafiati
Publicado: 23 de novembro de 2024

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Artigos de revistas sobre o assunto "Histopathologie digitale"

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Braun, Stephan A., e Doris Helbig. "Infantile digitale Fibromatose: ein seltener myofibrozytärer Tumor mit charakteristischer Histopathologie". JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, n.º 12 (dezembro de 2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450_suppl.

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Cummins, Donna M., Iskander H. Chaudhry e Matthew Harries. "Scarring Alopecias: Pathology and an Update on Digital Developments". Biomedicines 9, n.º 12 (24 de novembro de 2021): 1755. http://dx.doi.org/10.3390/biomedicines9121755.

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Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing these conditions. We provide insight from clinical and pathology practice into how diagnoses are derived and the key histological features observed across the most common PCAs seen in practice. Additionally, we highlight the opportunities that have emerged with advances in digital pathology and how these technologies may be used to develop clinicopathological relationships, improve working practices, enhance remote learning, reduce inefficiencies, optimise diagnostic yield, and harness the potential of artificial intelligence (AI).
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Tawfeeq, Furat Nidhal, Nada A. S. Alwan e Basim M. Khashman. "Optimization of Digital Histopathology Image Quality". IAES International Journal of Artificial Intelligence (IJ-AI) 7, n.º 2 (20 de abril de 2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.

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<span lang="EN-US">One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.</span>
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Amgad Mohamed Khater, Nesma. "Review on Advancements in Histopathology Education through Virtual Labs, Digital Microscopy and AI". International Journal of Science and Research (IJSR) 13, n.º 11 (5 de novembro de 2024): 807–8. http://dx.doi.org/10.21275/sr241113034953.

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Min, Eunjung, Nurbolat Aimakov, Sangjin Lee, Sungbea Ban, Hyunmo Yang, Yujin Ahn, Joon S. You e Woonggyu Jung. "Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining". Biomedical Optics Express 14, n.º 5 (18 de abril de 2023): 2068. http://dx.doi.org/10.1364/boe.484516.

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Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.
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Ciga, Ozan, Tony Xu e Anne Louise Martel. "Self supervised contrastive learning for digital histopathology". Machine Learning with Applications 7 (março de 2022): 100198. http://dx.doi.org/10.1016/j.mlwa.2021.100198.

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Amrania, Hemmel, Giuseppe Antonacci, Che-Hung Chan, Laurence Drummond, William R. Otto, Nicholas A. Wright e Chris Phillips. "Digistain: a digital staining instrument for histopathology". Optics Express 20, n.º 7 (15 de março de 2012): 7290. http://dx.doi.org/10.1364/oe.20.007290.

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Huss, Ralf, e Sarah E. Coupland. "Software‐assisted decision support in digital histopathology". Journal of Pathology 250, n.º 5 (25 de fevereiro de 2020): 685–92. http://dx.doi.org/10.1002/path.5388.

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Martines, Roosecelis B., Jana M. Ritter, Joy Gary, Wun-Ju Shieh, Jaume Ordi, Martin Hale, Carla Carrilho et al. "Pathology and Telepathology Methods in the Child Health and Mortality Prevention Surveillance Network". Clinical Infectious Diseases 69, Supplement_4 (9 de outubro de 2019): S322—S332. http://dx.doi.org/10.1093/cid/ciz579.

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Abstract This manuscript describes the Child Health and Mortality Prevention Surveillance (CHAMPS) network approach to pathologic evaluation of minimally invasive tissue sampling (MITS) specimens, including guidelines for histopathologic examination and further diagnostics with special stains, immunohistochemistry, and molecular testing, as performed at the CHAMPS Central Pathology Laboratory (CPL) at the Centers for Disease Control and Prevention, as well as techniques for virtual discussion of these cases (telepathology) with CHAMPS surveillance locations. Based on review of MITS from the early phase of CHAMPS, the CPL has developed standardized histopathology-based algorithms for achieving diagnoses from MITS and telepathology procedures in conjunction with the CHAMPS sites, with the use of whole slide scanners and digital image archives, for maximizing concurrence and knowledge sharing between site and CPL pathologists. These algorithms and procedures, along with lessons learned from initial implementation of these approaches, guide pathologists at the CPL and CHAMPS sites through standardized diagnostics of MITS cases, and allow for productive, real-time case discussions and consultations.
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Mungenast, Felicitas, Achala Fernando, Robert Nica, Bogdan Boghiu, Bianca Lungu, Jyotsna Batra e Rupert C. Ecker. "Next-Generation Digital Histopathology of the Tumor Microenvironment". Genes 12, n.º 4 (7 de abril de 2021): 538. http://dx.doi.org/10.3390/genes12040538.

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Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology—which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.
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Mais fontes

Teses / dissertações sobre o assunto "Histopathologie digitale"

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Nisar, Zeeshan. "Self-supervised learning in the presence of limited labelled data for digital histopathology". Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD016.

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Un défi majeur dans l'application de l'apprentissage profond à l'histopathologie réside dans la variation des colorations, à la fois inter et intra-coloration. Les modèles d'apprentissage profond entraînés sur une seule coloration (ou domaine) échouent souvent sur d'autres, même pour la même tâche (par exemple, la segmentation des glomérules rénaux). L'annotation de chaque coloration est coûteuse et chronophage, ce qui pousse les chercheurs à explorer des méthodes de transfert de coloration basées sur l'adaptation de domaine. Celles-ci visent à réaliser une segmentation multi-coloration en utilisant des annotations d'une seule coloration, mais sont limitées par l'introduction d'un décalage de domaine, réduisant ainsi les performances. La détection et la quantification de ce décalage sont essentielles. Cette thèse se concentre sur des méthodes non supervisées pour développer une métrique de détection du décalage et propose une approche de transfert de coloration pour le minimiser. Bien que ces algorithmes réduisent le besoin d'annotations, ils peuvent être limités pour certains tissus. Cette thèse propose donc une amélioration via l'auto-supervision
A key challenge in applying deep learning to histopathology is the variation in stainings, both inter and intra-stain. Deep learning models trained on one stain (or domain) often fail on others, even for the same task (e.g., kidney glomeruli segmentation). Labelling each stain is expensive and time-consuming, prompting researchers to explore domain adaptation based stain-transfer methods. These aim to perform multi-stain segmentation using labels from only one stain but are limited by the introduction of domain shift, reducing performance. Detecting and quantifying this domain shift is important. This thesis focuses on unsupervised methods to develop a metric for detecting domain shift and proposes a novel stain-transfer approach to minimise it. While multi-stain algorithms reduce the need for labels in target stains, they may struggle with tissue types lacking source-stain labels. To address this, the thesis focuses to improve multi-stain segmentation with less reliance on labelled data using self-supervision. While this thesis focused on kidney glomeruli segmentation, the proposed methods are designed to be applicable to other histopathology tasks and domains, including medical imaging and computer vision
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Laifa, Oumeima. "A joint discriminative-generative approach for tumour angiogenesis assessment in computational pathology". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS230.

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L’angiogenèse est le processus par lequel de nouveaux vaisseaux sanguins se forment à partir du réseaux préexistant. Au cours de l’angiogenèse tumorale, les cellules tumorales sécrètent des facteurs de croissance qui activent la prolifération et la migration des cellules et stimulent la surproduction du facteur de croissance endothélial vasculaire (VEGF). Le rôle fondamental de l’approvisionnement vasculaire dans la croissance tumorale et le developement des thérapies anticancéreuses rend l’évaluation de l’angiogenèse tumorale, cruciale dans l’évaluation de l’effet des thérapies anti-angiogéniques, en tant que thérapie anticancéreuse prometteuse. Dans cette étude, nous établissons un panel quantitatif et qualitatif pour évaluer les structures des vaisseaux sanguins de la tumeur sur des images de fluorescence non invasives et des images histopathologique sur toute la surface tumorale afin d’identifier les caractéristiques architecturales et les mesures quantitatives souvent associées à la réponse thérapeutique ou prédictive de celle-ci. Nous développons un pipeline formé de Markov Random Field (MFR) et Watershed pour segmenter les vaisseaux sanguins et les composants du micro-environnement tumoral afin d’évaluer quantitativement l’effet du médicament anti-angiogénique Pazopanib sur le système vasculaire tumoral et l’interaction avec le micro-environnement de la tumeur. Le pazopanib, agent anti-angiogénèse, a montré un effet direct sur le système vasculaire du réseau tumoral via les cellules endothéliales. Nos résultats montrent une relation spécifique entre la néovascularisation apoptotique et la densité de noyau dans une tumeur murine traitée par Pazopanib. Une évaluation qualitative des vaisseaux sanguins de la tumeur est réalisée dans la suite de l’étude. Nous avons développé un modèle de réseau de neurone discriminant-générateur basé sur un modele d’apprentissage : réseau de neurones convolutionnels (CNN) et un modèle de connaissance basé sur des règles Marked Point Process (MPP) permettant de segmenter les vaisseaux sanguins sur des images très hétérogènes à l’aide de très peu de données annotées. Nous détaillons l’intuition et la conception du modèle discriminatif-génératif, sa similarité avec les Réseaux antagonistes génératifs (GAN) et nous évaluons ses performances sur des données histopathologiques et synthétiques. Les limites et les perspectives de la méthode sont présentées à la fin de notre étude
Angiogenesis is the process through which new blood vessels are formed from pre-existing ones. During angiogenesis, tumour cells secrete growth factors that activate the proliferation and migration of endothelial cells and stimulate over production of the vascular endothelial growth factor (VEGF). The fundamental role of vascular supply in tumour growth and anti-cancer therapies makes the evaluation of angiogenesis crucial in assessing the effect of anti-angiogenic therapies as a promising anti-cancer therapy. In this study, we establish a quantitative and qualitative panel to evaluate tumour blood vessels structures on non-invasive fluorescence images and histopathological slide across the full tumour to identify architectural features and quantitative measurements that are often associated with prediction of therapeutic response. We develop a Markov Random Field (MFRs) and Watershed framework to segment blood vessel structures and tumour micro-enviroment components to assess quantitatively the effect of the anti-angiogenic drug Pazopanib on the tumour vasculature and the tumour micro-enviroment interaction. The anti-angiogenesis agent Pazopanib was showing a direct effect on tumour network vasculature via the endothelial cells crossing the whole tumour. Our results show a specific relationship between apoptotic neovascularization and nucleus density in murine tumor treated by Pazopanib. Then, qualitative evaluation of tumour blood vessels structures is performed in whole slide images, known to be very heterogeneous. We develop a discriminative-generative neural network model based on both learning driven model convolutional neural network (CNN), and rule-based knowledge model Marked Point Process (MPP) to segment blood vessels in very heterogeneous images using very few annotated data comparing to the state of the art. We detail the intuition and the design behind the discriminative-generative model, and we analyze its similarity with Generative Adversarial Network (GAN). Finally, we evaluate the performance of the proposed model on histopathology slide and synthetic data. The limits of this promising framework as its perspectives are shown
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Khan, Adnan M. "Algorithms for breast cancer grading in digital histopathology images". Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66024/.

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Histological analysis of tissue biopsies by an expert pathologist is considered gold standard for diagnosing many cancers, including breast cancer. Nottingham grading system, which is the most widely used criteria for histological grading of breast tissues, consists of three components: mitotic count, nuclear atypia and tubular formation. In routine histological analysis, pathologists perform grading of breast cancer tissues by manually examining each tissue specimen against the three components, which is a laborious and subjective process and thus can suffer from low inter-observer agreement. With the advent of digital whole-slide scanning platforms, automatic image analysis algorithms can be used as a partial solution for these issues. The main goal of this dissertation is to develop frameworks that can aid towards building an automated or semi-automated breast cancer grading system. We present novel frameworks for detection of mitotic cells and nuclear atypia scoring in breast cancer histopathology images. Both of these frameworks can play a fundamental role in developing a computer-assisted breast cancer grading system. Moreover, the proposed image analysis frameworks can be adapted to grading and analysis of cancers of several other tissues such as lung and ovarian cancers. In order to deal with one of the fundamental problems in histological image analysis applications, we first present a stain normalisation algorithm that minimises the staining inconsistency in histological images. The algorithm utilises a novel image-specific colour descriptor which summarises the colour contents of a histological image. Stain normalisation algorithm is used in the remainder of the thesis as a preprocessing step. We present a mitotic cell detection framework mimicking a pathologist’s approach, whereby we first perform tumour segmentation to restrict our search for mitotic cells to tumour regions only, followed by candidate detection and evaluation in a statistical machine learning framework. We also employ a discriminative dictionary learning paradigm to learn the visual appearance of mitotic cells, that models colour, texture, and shape in a composite manner. Finally, we present a nuclear atypia scoring framework based on a novel image descriptor which summarises the texture heterogeneity, inherent in histological images in a compact manner. Classification is performed using a geodesic k-nearest neighbour classifier which explicitly exploits the structure of Riemannian manifold of the descriptor and achieves significant performance boost as compared to Euclidean counterpart.
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Liu, Jingxin. "Stain separation, cell classification and histochemical score in digital histopathology images". Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52290/.

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This thesis focuses on developing new automatic techniques addressing three typical problems in digital histopathology image analysis, histochemical stain separation at pixel-level, cell classifications at region level, and histochemical score assessment at image level, with the aim of providing useful tools to help histopathologists in their decision making. First, we study a pixel-level problem, separating positive chemical stains. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this thesis, we show that the colour distribution of the positive immunohistochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this observation, two novel luminance adaptive biomarker detection methods are proposed. The first, termed Luminance Adaptive Multi-Thresholding (LAMT) first separate the pixels according to their luminance levels and for each luminance level a separate threshold is found for detecting the positive stains. The second, termed Luminance Adaptive Random Forest (LARF) applies one of the most powerful machine learning models, random forest, as a base classifier to build an ensemble classifier for biomarker detection. Second, we study a cell-level problem, the cell classification task in pathology images. Two different classification models are proposed. The first model for HEp-2 cell pattern classification comes with a novel object-graph based feature, which decompose the binary image into primitive objects and represent them with a set of morphological feature. Work on cell classification is further extended using deep learning model termed Deep Autoencoding-Classification Network (DACN). The DACN model consists of an autoencoder and a conventional classification convolutional neural network (CNN) with the two sharing the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error based on a multi-task learning procedure. We will present experiment results to show that the proposed DACN outperforms all known state-of-the-art on two public indirect immunofluorescence stained HEp-2 cell datasets and H\&E stained colorectal adenocarcinomas cell dataset. Third, we study an image-level problem, assessing the histochemical score of a histopathology image. To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers by assigning a histochemical score (H-Score) to each TMA core with a semi-quantitative assessment method. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this thesis, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region, a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
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Kårsnäs, Andreas. "Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis". Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219306.

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In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.
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Lakhotia, Kritika. "Visualization and quantification of 3D tumor-host interface architecture reconstructed from digital histopathology slides". Thesis, State University of New York at Buffalo, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10127616.

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Oral cavity cancer (OCC) is a type of cancer of the lip, tongue, salivary glands and other sites in the mouth (buccal or oral cavity) and is the sixth leading cause of cancer worldwide. Patients with OCC are treated based on a staging system: low-stage patients typically receive less aggressive therapy compared to high-stage patients. Unfortunately, low-stage patients are sometimes at risk for locoregional recurrence. Recently, a semi-quantitative risk scoring system has been developed to assess the locoregional recurrence risk for low-stage patients. This risk scoring system is based on tissue characteristics determined on 2D histopathology images under a microscope. This modality limits the appreciation of the 3D architecture of the tumor and its associated morphological features. This thesis aims to visualize 3D models of the tumor-host interface reconstructed from serially-sectioned histopathology slides and quantify their clinically validated morphological features to predict locoregional recurrence after treatment. The 3D models are developed and quantified for 6 patient cases using readily available tools. This pilot study provides a framework for an automated diagnostic technique for 3D visualization and morphological analysis of tumor biology which is traditionally done using 2D analysis.

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Kampouraki, Vasileia. "Patch-level classification of brain tumor tissue in digital histopathology slides with Deep Learning". Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177361.

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Histopathology refers to the visual inspection of tissue under the microscope and it is the core part of diagnosis. The process of manual inspection of histopathology slides is very time-consuming for pathologists and error-prone. Furthermore, diagnosis can sometimes differ among specialists. In recent years, convolutional neural networks (CNNs) have demonstrated remarkable performances in the classification of digital histopathology images. However, due to their high resolution, whole-slide images are of immense size, often gigapixels, making it infeasible to train CNNs directly on them. For that, patch-level classification is used instead. In this study, a deep learning approach for patch-level classification of glioblastoma (i.e. brain cancer) tissue is proposed. Four different state-of-the-art models were evaluated (MobileNetV2, ResNet50, ResNet152V2, and VGG16), with MobileNetV2 being the best among them, achieving 80% test accuracy. The study also proposes a scratch-trained CNN architecture, inspired by the popular VGG16 model, which achieved 81% accuracy. Both models scored 87% test accuracy when trained with data augmentation. All models were trained and tested on randomly sampled patches from the Ivy GAP dataset, which consisted of 724 H&E images in total. Finally, as patch-level predictions cannot be used explicitly by pathologists, prediction results from two slides were presented in the form of whole-slide images. Post-processing was also performed on those two predicted WSIs in order to make use of the spatial correlations among the patches and increase the classification accuracy. The models were statistically compared using the Wilcoxon signed-rank test.
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Naylor, Peter. "Du phénotypage cellulaire à la classification de lames digitales : Une application au traitement du cancer du sein triple-négatif". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM051.

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Le 21ème siècle a vu l'essor de la pathologie numérique. De fait, les challenges de l'analyse des données histopathologiques ont contribué à un effort mondial dans la lutte globale contre le cancer. Parallèlement, le succès récent de la décision par automate, plus particulièrement l'apprentissage profond, a révolutionné la recherche dans le domaine de la vision par ordinateur. Dans cette thèse, nous avons étudié la prédiction de la réponse au traitement chez des patients atteints d'un cancer du sein triple négatif avec deux approches différentes aux performances similaires. La première approche, basée sur le récent succès de la vision par ordinateur, extrait des caractéristiques afin d'en effectuer la classification finale. La deuxième approche contraint le flux d'information à passer par la segmentation de noyaux. En particulier, elle permet d'incorporer des informations de haute résolution à une vue globale basse résolution. Bien que cette approche soit plus attrayante, puisqu'elle repose sur l'analyse et la quantification d'un élément biologique précis, la segmentation de noyaux est une tâche fastidieuse. Nous proposons une nouvelle approche de segmentation par apprentissage profond, qui est particulièrement adaptée à la séparation de cellules en contact
The rise of digital pathology and with it the challenges of histopathology analysis have been the focus of a worldwide effort in the overall fight against cancer. In parallel, the recent success of automated decision-making, machine learning, and specifically deep learning, have revolutionised the basis of research as we know today. In this thesis, we tackle the prediction of treatment response in triple-negative breast cancer patients with two different approaches that reach similar outcomes. The first line of approach, based on the recent success of computer vision, extracts learned features from the data in order to perform classification. The second line of approach forces the information flow to pass through nuclei segmentation. In particular, it allows the incorporation of high-resolution information on to a lower resolution overview. Yet while this approach is more appealing as it is based on the analysis and quantification of a precise biological element, nuclei segmentation is troublesome. While solving the task of nuclei segmentation with deep learning, we propose a new formulation for nuclei segmentation which excels at separating touching objects
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Hossain, Md Shamim. "An automated deep learning based approach for nuclei segmentation of renal digital histopathology image analysis". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2611.

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Renal clear cell carcinoma affects the kidneys by abnormal cell division which spreads to other organs through the bloodstream and lymphatic system. The number of renal cancer cases grows whilst rapid and accurate diagnoses are required for early intervention. Biopsies are critical for cancer diagnosis. Pathologists look beyond manual evaluation to include computer-based analysis to develop accurate cancer diagnostics. Pathologists render diagnostic reports to assist with treatment whilst expert analysis is time consuming and restricts early diagnosis. The process of manual expert pathology reporting is prohibitive and poor and repetitive concentration can lead to misdiagnosis. The probability of observational error increases along with the increased workload of the average pathologist and the demand for histopathology image analysis. Advances in computational and computer-assisted applications can provide accurate and timely analysis of histopathology images. Manual annotation now looks to machine learning algorithms. The nuclei segmentation technique is one possible approach. It uses deep learning-based nuclei segmentation approaches to train networks. This assists expert pathology which is expensive and time-consuming. Overlapping nuclei segmentation is a challenging issue for automated histopathology image analysis. Deep observation is required in digital images to identify the overlapping nuclei and variations in segmentation errors can mislead expert pathologists. The aim of this research study was to perform a literature review of existing nuclei segmentation techniques including overlapping splitting algorithms; identify the limitations and knowledge gaps; and propose computerised deep learning based individual nuclei segmentation and analysis of histopathology images. A mixed method research study was performed with sequential research experiments in data collection; image pre-processing; synthetic image generation; segmentation of nuclei regions; overlapping nuclei; and the validation of a proposed framework. A series of experiments were executed to find the most viable approach. An improved approach was designed for synthetic image generation using a cycle-consistent GAN network. The network created synthetic backgrounds and allowed for a CNN filtering method to separate the initial synthetic backgrounds. Nuclei shapes were collected to create transformed shapes. These transformed shapes were placed on the refined synthetic backgrounds to generate complete synthetic images. The similarity of original and synthetic images established and viable, valid pathway. A nuclei mask of synthetic images was collected to train a modified U-net segmentation network for better segmentation accuracy. These synthetic images performed better than original images. Accurately delineating the individual nucleus boundary helped to generatean automated system divide the nuclei clumps into individual nuclei in histopathology images. Using the nuclei ground truth of original images, it was possible to validate an application that informed manual expert pathology and to process multiple images and minimise histopathology image analysis. The novelty of this research is the creation of an automated deep learning based individual nuclei segmentation system for renal histopathology images. The synthetic images and corresponding nuclei masks were trained with a modified U-net nuclei segmentation network. The trained network provides better nuclei segmentation performance in original images. The research developed a robust application which allows for the analysis of multiple histopathology images.
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Irshad, Humayun. "Automated Mitosis Detection in Color and Multi-spectral High-Content Images in Histopathology : Application to Breast Cancer Grading in Digital Pathology". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM007/document.

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La gradation de lames de biopsie fournit des informations pronostiques essentielles pour le diagnostic et le traitement. La détection et le comptage manuel des mitoses est un travail fastidieux, sujet à des variations inter-et intra- observateur considérables. L'objectif principal de cette thèse de doctorat est le développement d'un système capable de fournir une détection des mitoses sur des images provenant de différents types de scanners rapides automatiques, ainsi que d'un microscope multispectral. L'évaluation des différents systèmes proposés est effectuée dans le cadre du projet MICO (MIcroscopie COgnitive, projet ANR TecSan piloté par notre équipe). Dans ce contexte, les systèmes proposés ont été testés sur les données du benchmark MITOS. En ce qui concerne les images couleur, notre système s'est ainsi classé en deuxième position de ce concours international, selon la valeur du critère F-mesure. Par ailleurs, notre système de détection de mitoses sur images multispectrales surpasse largement les meilleurs résultats obtenus durant le concours
Digital pathology represents one of the major and challenging evolutions in modernmedicine. Pathological exams constitute not only the gold standard in most of medicalprotocols, but also play a critical and legal role in the diagnosis process. Diagnosing adisease after manually analyzing numerous biopsy slides represents a labor-intensive workfor pathologists. Thanks to the recent advances in digital histopathology, the recognitionof histological tissue patterns in a high-content Whole Slide Image (WSI) has the potentialto provide valuable assistance to the pathologist in his daily practice. Histopathologicalclassification and grading of biopsy samples provide valuable prognostic information thatcould be used for diagnosis and treatment support. Nottingham grading system is thestandard for breast cancer grading. It combines three criteria, namely tubule formation(also referenced as glandular architecture), nuclear atypia and mitosis count. Manualdetection and counting of mitosis is tedious and subject to considerable inter- and intrareadervariations. The main goal of this dissertation is the development of a framework ableto provide detection of mitosis on different types of scanners and multispectral microscope.The main contributions of this work are eight fold. First, we present a comprehensivereview on state-of-the-art methodologies in nuclei detection, segmentation and classificationrestricted to two widely available types of image modalities: H&E (HematoxylinEosin) and IHC (Immunohistochemical). Second, we analyse the statistical and morphologicalinformation concerning mitotic cells on different color channels of various colormodels that improve the mitosis detection in color datasets (Aperio and Hamamatsu scanners).Third, we study oversampling methods to increase the number of instances of theminority class (mitosis) by interpolating between several minority class examples that lietogether, which make classification more robust. Fourth, we propose three different methodsfor spectral bands selection including relative spectral absorption of different tissuecomponents, spectral absorption of H&E stains and mRMR (minimum Redundancy MaximumRelevance) technique. Fifth, we compute multispectral spatial features containingpixel, texture and morphological information on selected spectral bands, which leveragediscriminant information for mitosis classification on multispectral dataset. Sixth, we performa comprehensive study on region and patch based features for mitosis classification.Seven, we perform an extensive investigation of classifiers and inference of the best one formitosis classification. Eight, we propose an efficient and generic strategy to explore largeimages like WSI by combining computational geometry tools with a local signal measureof relevance in a dynamic sampling framework.The evaluation of these frameworks is done in MICO (COgnitive MIcroscopy, ANRTecSan project) platform prototyping initiative. We thus tested our proposed frameworks on MITOS international contest dataset initiated by this project. For the color framework,we manage to rank second during the contest. Furthermore, our multispectral frameworkoutperforms significantly the top methods presented during the contest. Finally, ourframeworks allow us reaching the same level of accuracy in mitosis detection on brightlightas multispectral datasets, a promising result on the way to clinical evaluation and routine
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Livros sobre o assunto "Histopathologie digitale"

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Y, Mary J., Rigaut J. P, Unité de recherches biomathématiques et biostatistiques., Institut national de la santé et de la recherche médicale., Association pour la recherche sur le cancer. e European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology. Amsterdam: Elsevier Science, 1986.

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Y, Mary J., Rigaut J. P, Institut national de la santé et de la recherche médicale (France). Unité de recherches biomathématiques et biostatistiques., Association pour le développment de la recherche sur le cancer (France) e European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology: Based on a symposium. Amsterdam: Elsevier, 1986.

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(Editor), L. Frappart, B. Fontaniere (Editor) e E. Lucas (Editor), eds. Histopathology and Cytopathology of the Uterine Cervix: Digital Atlas. International Agency for Research on Cancer, 2004.

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Sybert, Virginia P. Disorders of Epidermal Appendages. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190276478.003.0003.

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Chapter 3 starts by covering conditions of the hair, including Alopecias (Loose Anagen Hair, Male Pattern Baldness, and Marie Unna Syndrome), Hirsutism (Gingival Fibromatosis and Hypertrichosis, Hypertrichosis Lanuginosa Congenita, Leprechaunism, and Localized Hypertrichosis), and Hair Shaft Abnormalities (including Monilethrix, Pili Annulati, Pili Torti, Pili Trianguli Et Canaliculi, Trichorrhexis Invaginata, Trichorrhexis Nodosa, Woolly Hair, Menkes Disease, Trichodentoosseous Syndrome, Trichorhinophalangeal Syndrome, and Trichothiodystrophy). It then covers conditions of the nails, including Congenital Malalignment of the Great Toenails, Familial Dystrophic Shedding of the Nails, Leukonychia, Twenty-Nail Dystrophy, Nail-Patella Syndrome, Onychotrichodysplasia and Neutropenia, and Pachyonychia Congenita). Conditions of the Sweat Glands (Hidradenitis Suppurativa, Hyperhidrosis, and Multiple Syringomas), Sebaceous Glands (Eruptive Vellus Hair Cysts, Familial Dyskeratotic Comedones, Oral-Facial-Digital Syndrome Type I, and Steatocystoma Multiplex), and Ectodermal Dysplasia Syndromes (AEC Syndrome, Clouston Syndrome, EEC Syndrome, Focal Facial Ectodermal Dysplasia, GAPO Syndrome, Hypohidrotic Ectodermal Dysplasia, and Tooth and Nail Syndrome) are also covered. Each condition is discussed in detail, including dermatologic features, associated anomalies, histopathology, basic defect, treatment, mode of inheritance, prenatal diagnosis, and differential diagnosis.
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Capítulos de livros sobre o assunto "Histopathologie digitale"

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Singh, Rishita, Nitu Dogra, Ravina Yadav, Angamba Meetei Potshangbam, Deepshikha Pande Katare e Ruchi Jakhmola Mani. "Digital Histopathology". In Handbook of AI-Based Models in Healthcare and Medicine, 347–77. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003363361-18.

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Chenni, Wafa, Habib Herbi, Morteza Babaie e Hamid R. Tizhoosh. "Patch Clustering for Representation of Histopathology Images". In Digital Pathology, 28–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23937-4_4.

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Khan, Umair Akhtar Hasan, Carolin Stürenberg, Oguzhan Gencoglu, Kevin Sandeman, Timo Heikkinen, Antti Rannikko e Tuomas Mirtti. "Improving Prostate Cancer Detection with Breast Histopathology Images". In Digital Pathology, 91–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23937-4_11.

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Babaie, Morteza, e Hamid R. Tizhoosh. "Deep Features for Tissue-Fold Detection in Histopathology Images". In Digital Pathology, 125–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23937-4_15.

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Dey, Pranab. "Digital Pathology". In Basic and Advanced Laboratory Techniques in Histopathology and Cytology, 195–203. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6616-3_18.

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Benomar, Mohammed Lamine, Nesma Settouti, Rudan Xiao, Damien Ambrosetti e Xavier Descombes. "Convolutional Neuronal Networks for Tumor Regions Detection in Histopathology Images". In Digital Technologies and Applications, 13–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73882-2_2.

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Rymarczyk, Dawid, Adam Pardyl, Jarosław Kraus, Aneta Kaczyńska, Marek Skomorowski e Bartosz Zieliński. "ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification". In Machine Learning and Knowledge Discovery in Databases, 421–36. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26387-3_26.

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AbstractThe rapid development of histopathology scanners allowed the digital transformation of pathology. Current devices fastly and accurately digitize histology slides on many magnifications, resulting in whole slide images (WSI). However, direct application of supervised deep learning methods to WSI highest magnification is impossible due to hardware limitations. That is why WSI classification is usually analyzed using standard Multiple Instance Learning (MIL) approaches, that do not explain their predictions, which is crucial for medical applications. In this work, we fill this gap by introducing ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability, as confirmed by the experiments conducted on five recognized whole-slide image datasets.
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Xu, Jun, Chengfei Cai, Yangshu Zhou, Bo Yao, Geyang Xu, Xiangxue Wang, Ke Zhao, Anant Madabhushi, Zaiyi Liu e Li Liang. "Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net". In Digital Pathology, 100–108. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23937-4_12.

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Dey, Pranab. "Digital Image Analysis and Virtual Microscopy in Pathology". In Basic and Advanced Laboratory Techniques in Histopathology and Cytology, 185–92. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8252-8_18.

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Hering, Jan, e Jan Kybic. "Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology". In Lecture Notes in Computer Science, 274–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50516-5_24.

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Trabalhos de conferências sobre o assunto "Histopathologie digitale"

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Anand, Deepak, Shrey Gadiya e Amit Sethi. "Histographs: graphs in histopathology". In Digital Pathology, editado por John E. Tomaszewski e Aaron D. Ward. SPIE, 2020. http://dx.doi.org/10.1117/12.2550114.

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Ashman, Kimberly, Brian Summa, Sharon Fox e J. Quincy Brown. "Visualizing Analog and Digital Diagnostic Provenance in Pathology". In Microscopy Histopathology and Analytics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/microscopy.2022.mw4a.6.

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Cunningham, Brian T. "Photonic Crystal Resonant Coupling to Nanoantennas and Applications for Digital Resolution Biosensing". In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/microscopy.2018.mw2a.3.

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Ashman, Kimberly, Max S. Cooper, Sharon Fox, Brian Summa e J. Quincy Brown. "Reconstructing a Multiscale Digital Record of Clinical Pathology Slide Review Using Onboard-Cameras". In Microscopy Histopathology and Analytics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/microscopy.2022.mw4a.2.

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Gupta, Deven K., Trey Highland, David A. Miller e Adam Wax. "Utilizing Quantitative Phase Microscopy to Localize Fluorescence Imaging Using the Transport of Intensity Equation". In Microscopy Histopathology and Analytics. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/microscopy.2024.mw3a.5.

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We demonstrate the use of quantitative phase microscopy to localize defocused fluorescent images with the transport of intensity equation. Specifically, we demonstrate a technique for digitally refocusing images from three-dimensional cell cultures.
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Li, Wenjing, Rich W. Lieberman, Sixiang Nie, Yihua Xie, Michael Eldred e Jody Oyama. "Histopathology reconstruction on digital imagery". In SPIE Medical Imaging, editado por Berkman Sahiner e David J. Manning. SPIE, 2009. http://dx.doi.org/10.1117/12.811344.

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Zhuge, Huimin, David Manthey, Kimberly Ashman, Brian Summa, Roni Choudhury e J. Quincy Brown. "Interactive WSI Review and Annotation Tracker, and Digital Visualization Tool for Pathologist Diagnosis of Whole Slide Images". In Microscopy Histopathology and Analytics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/microscopy.2022.mw3a.4.

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Han, Wenchao, Carol Johnson, Mena Gaed, Jose Gomez-Lemus, Madeleine Moussa, Joseph Chin, Stephen Pautler, Glenn Bauman e Aaron Ward. "Automatic high-grade cancer detection on prostatectomy histopathology images". In Digital Pathology, editado por John E. Tomaszewski e Aaron D. Ward. SPIE, 2019. http://dx.doi.org/10.1117/12.2512916.

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Han, Wenchao, Aaron D. Ward, Carol Johnson, José Gomez, Mena Gaed, Joseph Chin, Stephen Pautler, Glenn Bauman e Madeleine Moussa. "Automatic cancer detection and localization on prostatectomy histopathology images". In Digital Pathology, editado por Metin N. Gurcan e John E. Tomaszewski. SPIE, 2018. http://dx.doi.org/10.1117/12.2292450.

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Clarke, G. M., C. Peressotti, G. E. Mawdsley, S. Eidt, M. Ge, T. Morgan, J. T. Zubovits e M. J. Yaffe. "Three-dimensional digital breast histopathology imaging". In Medical Imaging, editado por Robert L. Galloway, Jr. e Kevin R. Cleary. SPIE, 2005. http://dx.doi.org/10.1117/12.593484.

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