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Автор: Grafiati
Опубліковано: 26 жовтня 2023

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Статті в журналах з теми "HISTOPATHOLOGY IMAGE"

1

Chen, Jia-Mei, Yan Li, Jun Xu, Lei Gong, Lin-Wei Wang, Wen-Lou Liu, and Juan Liu. "Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review." Tumor Biology 39, no. 3 (March 2017): 101042831769455. http://dx.doi.org/10.1177/1010428317694550.

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Анотація:
With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.
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2

Arevalo, John, Angel Cruz-Roa, and Fabio A. González O. "Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte." Revista Med 22, no. 2 (December 1, 2014): 79. http://dx.doi.org/10.18359/rmed.1184.

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<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>
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3

Wang, Pin, Shanshan Lv, Yongming Li, Qi Song, Linyu Li, Jiaxin Wang, and Hehua Zhang. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.3172.

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Анотація:
Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for the histopathology image classification. First, deep features are obtained by deep transfer network based on regularized loss functions. Then, the rotational sample subspace sampling is applied to increase the diversity between training sets. Subsequently, subspace projection learning is introduced to achieve dimensionality reduction. Finally, the ensemble learning is used for histopathology image classification. The proposed method was tested on 126 histopathology images of the mouse model. The experimental results demonstrate that the proposed method has achieved a remarkable classification accuracy (99.39%, 99.74%, 100%). It has demonstrated that the proposed approach is promising for early cancer diagnosis.
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4

Wang, Pin, Shanshan Lv, Yongming Li, Qi Song, Linyu Li, Jiaxin Wang, and Hehua Zhang. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.31722289.

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Анотація:
Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for the histopathology image classification. First, deep features are obtained by deep transfer network based on regularized loss functions. Then, the rotational sample subspace sampling is applied to increase the diversity between training sets. Subsequently, subspace projection learning is introduced to achieve dimensionality reduction. Finally, the ensemble learning is used for histopathology image classification. The proposed method was tested on 126 histopathology images of the mouse model. The experimental results demonstrate that the proposed method has achieved a remarkable classification accuracy (99.39%, 99.74%, 100%). It has demonstrated that the proposed approach is promising for early cancer diagnosis.
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5

Tawfeeq, Furat Nidhal, Nada A. S. Alwan, and Basim M. Khashman. "Optimization of Digital Histopathology Image Quality." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (April 20, 2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.

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Анотація:
<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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6

Gupta, Rachit Kumar, Jatinder Manhas, and Mandeep Kour. "Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images." JOURNAL OF SCIENTIFIC RESEARCH 66, no. 03 (2022): 219–26. http://dx.doi.org/10.37398/jsr.2022.660327.

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Анотація:
Detection and classification of cancerous tissue from histopathologic images is quite a challenging task for pathologists and computer assisted medical diagnosis systems because of the complexity of the histopathology image. For a good diagnostic system, feature extraction from the medical images plays a crucial role for better classification of images. Using inappropriate or redundant features leads to poor classification results because classification algorithm learns a lot of unimportant information from the images. We propose hybrid feature extractor using different feature extraction algorithms that can extract various types of features from histopathological image. For this study, feature fused Convolution Neural Network, Gray Level Cooccurrence Matrix, and Local Binary Pattern algorithms are used. The texture and deep features obtained from these methods are used as input vector to classifiers: Support Vector Machine, KNearest Neighbor, Naïve Bayes and Boosted Tree. Prediction results of these classifiers are combined using soft majority voting algorithm to predict final output. Proposed method achieved an accuracy of 98.71%, which is quite high as compared to previous similar research works. Proposed method was capable of identifying most of cancerous histopathology images. The combination of deep and textural features can be potentially used for creating computer assisted medical imaging diagnosis system that can detect cancer from histopathology images timely and accurately.
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7

Rani V, Sudha, and M. Jogendra Kumar. "Histopathological Image Classification Methods and Techniques in Deep Learning Field." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (December 31, 2022): 158–65. http://dx.doi.org/10.17762/ijritcc.v10i2s.5923.

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Анотація:
A cancerous tumour in a woman's breast, Histopathology detects breast cancer. Histopathological images are a hotspot for medical study since they are difficult to judge manually. In addition to helping doctors identify and treat patients, this image classification can boost patient survival. This research addresses the merits and downsides of deep learning methods for histopathology imaging of breast cancer. The study's histopathology image classification and future directions are reviewed. Automatic histopathological image analysis often uses complete supervised learning where we can feed the labeled dataset to model for the classification. The research methods are frequentlytrust on feature extraction techniques tailored to specific challenges, such as texture, spatial, graph-based, and morphological features. Many deep learning models are also created for picture classification. There are various deep learning methods for classifying histopathology images.
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Tellez, David, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 2 (February 1, 2021): 567–78. http://dx.doi.org/10.1109/tpami.2019.2936841.

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9

Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (February 19, 2023): 2307. http://dx.doi.org/10.3390/s23042307.

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Анотація:
This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.
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10

Kandel, Ibrahem, Mauro Castelli, and Aleš Popovič. "Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images." Journal of Imaging 6, no. 9 (September 8, 2020): 92. http://dx.doi.org/10.3390/jimaging6090092.

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Анотація:
The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.
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Більше джерел

Дисертації з теми "HISTOPATHOLOGY IMAGE"

1

Chaganti, Shikha. "Image Analysis of Glioblastoma Histopathology." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406820611.

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2

DI, CATALDO SANTA. "Image Processing Techniques for Histopathology." Doctoral thesis, Politecnico di Torino, 2011. http://hdl.handle.net/11583/2586367.

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Анотація:
In the last few years biologists and pathologists are relying more and more on image analysis, and immunohistochemistry (IHC) is nowadays one of the most popular imaging techniques to analyze the presence and activity of target antigens in the tissues, with important applications in the diagnosis and assessment of tumors as well as for several research purposes. However, immunohistochemistry has been traditionally affected by lack of reproducibility due to technological variabilities as well as to the inherent subjectivity of the visual observation, thus the analysis has been limited to qualitative evaluation of the presence of the target stains within the tissues. The rapid evolution of the technique as a valid diagnostic and prognostic tool for tumor marker identification and cancer assessment has ultimately shifted the aim from qualitative to quantitative, stressing the demand for the standardization of the overall IHC assay and for the extraction of objective and repeatable measures of protein activity from the IHC images. Computer-aided image analysis has been universally acknowledged for having a fundamental role in solving the IHC standardization issue; in particular, tissue and cell segmentation techniques are precious instruments to identify the regions of interest of the target antigens in the specimens, allowing fully-automated and repeatable measurements at cellular and sub-cellular level; this is required by modern pathology and not feasible with simple visual evaluation. To this date, literature does not provide effective solutions for these challenging tasks, which gives motivation to our thesis work. This thesis addresses the problems of tissue compartmentalization and cell segmentation in IHC images, proposing fully-automated techniques to i) recognize the cancerous areas of the samples disregarding non-interesting tissues such as stroma and blood vessels; ii) detect and delineate the sub-cellular regions of the cancerous cells, i.e. nuclei, cellular membranes and cytoplasm. The proposed methods, based on color and morphological processing, were validated on large datasets of IHC cancer images from several anatomical locations and compared experimentally with state of the art segmentation approaches, such as Support Vector Machines and active contours. Our extensive experimental results demonstrate the quantitative accuracy and reproducibility of the segmentations provided by our techniques, that can be used to obtain localized measure of protein activity as well as for any other applications requiring tissue and cell exploration in pathological tissues. We conclude our thesis with final remarks about IHC quantification methods, proposing a set of requirements to obtain reliable quantifications applying computer-aided image analysis.
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3

Sertel, Olcay. "Image Analysis for Computer-aided Histopathology." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276791696.

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4

Haddad, Jane Wurster 1965. "Evaluation of diagnostic clues in histopathology through image processing techniques." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/277296.

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Анотація:
The primary method for the diagnostic interpretation of histopathologic sections is visual analysis. However, in a small, but significant percentage of cases, histopathologists do not come to a consensus. Therefore, due to the importance of early and accurate detection of tissue changes indicative of pathology, quantitative image analysis techniques have been applied to this problem. The accurate segmentation of image structures such as cells and glands in histopathological sections, as with all "natural scenes", proves challenging. This has led to the development of an additional segmentation technique, the heuristic gradient search. Following the successful segmentation and labeling of scene objects, algorithms evaluating diagnostic clues as to the shape, size and distribution of image components were developed in order to form an overall diagnosis. A description of these diagnostic clues and the image processing techniques residing in the computer vision system used to evaluate them are presented.
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5

Traore, Lamine. "Semantic modeling of an histopathology image exploration and analysis tool." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066621/document.

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Анотація:
La formalisation des données cliniques est réalisée et adoptée dans plusieurs domaines de la santé comme la prévention des erreurs médicales, la standardisation, les guides de bonnes pratiques et de recommandations. Cependant, la communauté n'arrive pas encore à tirer pleinement profit de la valeur de ces données. Le problème majeur reste la difficulté à intégrer ces données et des services sémantiques associés au profit de la qualité de soins. Objectif L'objectif méthodologique de ce travail consiste à formaliser, traiter et intégrer les connaissances d'histopathologie et d'imagerie basées sur des protocoles standardisés, des référentiels et en utilisant les langages du web sémantique. L'objectif applicatif est de valoriser ces connaissances dans une plateforme pour faciliter l'exploration des lames virtuelles (LV), améliorer la collaboration entre pathologistes et fiabiliser les systèmes d'aide à la décision dans le cadre spécifique du diagnostic du cancer du sein. Il est important de préciser que notre but n'est pas de remplacer le clinicien, mais plutôt de l'accompagner et de faciliter ses lourdes tâches quotidiennes : le dernier mot reste aux pathologistes. Approche Nous avons adopté une approche transversale pour la représentation formelle des connaissances d'histopathologie et d'imagerie dans le processus de gradation du cancer. Cette formalisation s'appuie sur les technologies du web sémantique
Semantic modelling of a histopathology image exploration and analysis tool. Recently, anatomic pathology (AP) has seen the introduction of several tools such as high-resolution histopathological slide scanners, efficient software viewers for large-scale histopathological images and virtual slide technologies. These initiatives created the conditions for a broader adoption of computer-aided diagnosis based on whole slide images (WSI) with the hope of a possible contribution to decreasing inter-observer variability. Beside this, automatic image analysis algorithms represent a very promising solution to support pathologist’s laborious tasks during the diagnosis process. Similarly, in order to reduce inter-observer variability between AP reports of malignant tumours, the College of American Pathologists edited 67 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are relevant in the context of a given organ-specific cancer and have to be reported by the pathologist. The associated protocol includes interpretation guidelines for most of the required observations. All these changes and initiatives bring up a number of scientific challenges such as the sustainable management of the available semantic resources associated to the diagnostic interpretation of AP images by both humans and computers. In this context, reference vocabularies and formalization of the associated knowledge are especially needed to annotate histopathology images with labels complying with semantic standards. In this research work, we present our contribution in this direction. We propose a sustainable way to bridge the content, features, performance and usability gaps between histopathology and WSI analysis
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6

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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Анотація:
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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7

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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Анотація:
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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8

Fanchon, Louise. "Autoradiographie quantitative d'échantillons prélevés par biopsie guidée par TEP/TDM : méthode et applications cliniques." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0018.

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Анотація:
Au cours des dix dernières années, l’utilisation de l’imagerie par tomographie par émission de positrons (TEP) s’est rapidement développée en oncologie. Certaines tumeurs non visibles en imagerie anatomique conventionnelle sont détectables en mesurant l'activité métabolique dans le corps humain par TEP. L’imagerie TEP est utilisée pour guider la délivrance de traitements locaux tels que par rayonnement ionisants ou ablation thermique. Pour la délivrance de ces traitements, segmenter la zone tumorale avec précision est primordial. Cependant, la faible résolution spatiale des images TEP rend la segmentation difficile. Plusieurs études ont démontré que la segmentation manuelle est sujette à une grande variabilité inter- et intra- individuelle et est fastidieuse. Pour ces raisons, de nombreux algorithmes de segmentation automatiques ont été développés. Cependant, peu de données fiables, avec des résultats histopathologiques existent pour valider ces algorithmes car il est expérimentalement difficile de les produire. Le travail méthodologique mis en place durant cette thèse a eu pour but de développer une méthode permettant de comparer les données histopathologiques aux données obtenue par TEP pour tester et valider des algorithmes de segmentation automatiques. Cette méthode consiste à réaliser des autoradiographies quantitatives de spécimens prélevés lors de biopsies guidées par TEP/tomodensitométrie (TDM); l’autoradiographie permettant d’imager la distribution du radiotraceur dans les échantillons avec une haute résolution spatiale. Les échantillons de tissus sont ensuite finement tranchés pour pouvoir être étudiés à l’aide d’un microscope. L’autoradiographie et les photomicrographes de l’échantillon de tissus sont ensuite recalés à l’image TEP, premièrement en les alignant avec l’aiguille à biopsie visible sur l’image TDM, puis en les transférant sur l’image TEP. Nous avons ensuite cherché à utiliser ces données pour tester deux algorithmes de segmentation automatique d'images TEP, le Fuzzy Locally Adaptive Bayesian (FLAB) développé au Laboratoire de Traitement de l'Information Médicale (LaTIM) à Brest, ainsi qu’une méthode de segmentation par seuillage. Cependant, la qualité de ces données repose sur la précision du recalage des images TEP, autoradiographiques et des micrographes. La principale source d’erreur dans le recalage de ces images venant de la fusion des images TEP/TDM, une méthode a été développée afin de quantifier la précision du recalage. Les résultats obtenus pour les patients inclus dans cette étude montrent que la précision de la fusion varie de 1.1 à 10.9 mm. En se basant sur ces résultats, les données ont été triées, pour finalement sélectionner les données acquises sur 4 patients jugées satisfaisantes pour tester les algorithmes de segmentation. Les résultats montrent qu’au point de la biopsie, les contours obtenus avec FLAB concordent davantage avec le bord de la lésion observé sur les micrographes. Cependant les deux méthodes de segmentation donnent des contours similaires, les lésions étant peu hétérogènes
During the last decade, positron emission tomography (PET) has been finding broader application in oncology. Some tumors that are non-visible in standard anatomic imaging like computerized tomography (CT) or ultrasounds, can be detected by measuring in 3D the metabolic activity of the body, using PET imaging. PET images can also be used to deliver localized therapy like radiation therapy or ablation. In order to deliver localized therapy, the tumor border has to be delineated with very high accuracy. However, the poor spatial resolution of PET images makes the segmentation challenging. Studies have shown that manual segmentation introduces a large inter- and intra- variability, and is very time consuming. For these reasons, many automatic segmentation algorithms have been developed. However, few datasets with histopathological information are available to test and validate these algorithms since it is experimentally difficult to produce them. The aim of the method developed was to evaluate PET segmentation algorithms against the underlying histopathology. This method consists in acquiring quantitative autoradiography of biopsy specimen extracted under PET/CT guidance. The autoradiography allows imaging the radiotracer distribution in the biopsy specimen with a very high spatial accuracy. Histopathological sections of the specimen can then obtained and observed under the microscope. The autoradiography and the micrograph of the histological sections can then be registered with the PET image, by aligning them first with the biopsy needle seen on the CT image and then transferring them onto the PET image. The next step was to use this dataset to test two PET automatic segmentation algorithms: the Fuzzy Locally Adaptive Bayesian (FLAB) developed at the Laboratory of Medical Information Processing (LaTIM) in Brest, France, as well as a fix threshold segmentation method. However, the reliability of the dataset produced depends on the accuracy of the registration of the PET, autoradiography and micrograph images. The main source of uncertainty in the registration of these images comes from the registration between the CT and the PET. In order to evaluate the accuracy of the registration, a method was developed. The results obtained with this method showed that the registration error ranges from 1.1 to 10.9mm. Based on those results, the dataset obtained from 4 patients was judged satisfying to test the segmentation algorithms. The comparison of the contours obtained with FLAB and with the fixed threshold method shows that at the point of biopsy, the FLAB contour is closer than that to the histopathology contour. However, the two segmentation methods give similar contours, because the lesions were homogeneous
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9

Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.

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Анотація:
Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained histology images using deep learning. The dataset is publicly available from TCGA, and 220 whole slide images were used in this study. Ground truth labels were only available on whole slide level, but due to their large size, they could not be processed by convolutional neural networks. Therefore, patches were extracted from the whole slide images in two sizes and fed into separate networks for training. Preprocessing steps ensured that irrelevant information about the background was excluded, and that the images were stain normalized. The patch-level predictions were then combined to slide level, and the classification performance was measured on a test set. Experiments were conducted about the usefulness of pre-trained CNN models and data augmentation techniques, and the best method was selected after statistical comparisons. Following the patch-level training, five slide aggregation approaches were studied, and compared to build a whole slide classifier model. Best performance was achieved when using small patches (336 x 336 pixels), pre-trained CNN model without frozen layers, and mirroring data augmentation. The majority voting slide aggregation method resulted in the best whole slide classifier with 91.7% test accuracy and 100% sensitivity. In many comparisons, however, statistical significance could not be shown because of the relatively small size of the test set.
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10

Azar, Jimmy. "Automated Tissue Image Analysis Using Pattern Recognition." Doctoral thesis, Uppsala universitet, Bildanalys och människa-datorinteraktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231039.

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Анотація:
Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches. We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization. Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis. Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections. Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.
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Книги з теми "HISTOPATHOLOGY IMAGE"

1

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., and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology. Amsterdam: Elsevier Science, 1986.

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2

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), and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology: Based on a symposium. Amsterdam: Elsevier, 1986.

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3

Chevanne, Marta, and Riccardo Caldini. Immagini di Istopatologia. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-023-8.

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This collection of images of Histopathology is the fruit of the authors' thirty years' experience in the performance of practical exercises in General Pathology. It is aimed at students attending lessons of General Pathology on the Degree Courses in Medical Surgery and Biological Sciences. It does not aspire either to be complete from the point of view of the various organic pathologies, or to replace direct and personal observation of the histological preparations through the microscope, but is rather intended as an aid to students preparing for the exam. It does not include the rudiments of cytology and microscopic anatomy, which it is assumed have already been mastered by those approaching General Histopathology, nor are histopathological phenomena systematically addressed, for which the reader is referred to textbooks on General Pathology. The 44 preparations presented here have been grouped in line with the main arguments of General Pathology: Cellular Degeneration, Inflammation, Neoplasia both benign and malign, and Vascular Pathology. They have been selected for their didactic significance and the simplicity and clarity of the lesions present, without taking into account the information to be derived from the clinical case history. The images of the preparations, in which the best possible quality of reproduction has been sought, are presented in progressive enlargements and are accompanied by brief descriptions comprising the explanations essential for identification of the characteristic aspects of the elementary lesion, as well as any eventual defects in the preparations themselves. Effectively, the objective of the work is to enable the student to exercise his understanding of the images. For this reason the casuistics included is as essential as possible, and the method of presentation utilised is designed to avoid mere visual memorisation, stimulating first analysis and then synthesis, and the development of individual logical skills so as to indicate whether aspects of cellular pathology, inflammation or neoplasia are present.
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4

Tibor, Tot, and Dean Peter B, eds. Breast cancer: The art and science of early detection with mammography : perception, interpretation, histopathologic correlation. Stuttgart: Thieme, 2005.

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Частини книг з теми "HISTOPATHOLOGY IMAGE"

1

Mohanty, Manoranjan, and Wei Tsang Ooi. "Histopathology Image Streaming." In Advances in Multimedia Information Processing – PCM 2012, 534–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_50.

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2

Chhoker, Ayush, Kunlika Saxena, Vipin Rai, and Vishwadeepak Singh Baghela. "Histopathology Osteosarcoma Image Classification." In Proceedings of International Conference on Recent Trends in Computing, 163–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8825-7_15.

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3

Ortega-Gil, Ana, Arrate Muñoz-Barrutia, Laura Fernandez-Terron, and Juan José Vaquero. "Tuberculosis Histopathology on X Ray CT." In Image Analysis for Moving Organ, Breast, and Thoracic Images, 169–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00946-5_18.

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4

Bueno, Gloria, Oscar Déniz, Jesús Salido, M. Milagro Fernández, Noelia Vállez, and Marcial García-Rojo. "Colour Model Analysis for Histopathology Image Processing." In Color Medical Image Analysis, 165–80. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5389-1_9.

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5

Shi, Xiaoshuang, Fuyong Xing, Yuanpu Xie, Hai Su, and Lin Yang. "Cell Encoding for Histopathology Image Classification." In Lecture Notes in Computer Science, 30–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_4.

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Wei, Jerry, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, et al. "A Petri Dish for Histopathology Image Analysis." In Artificial Intelligence in Medicine, 11–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77211-6_2.

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7

Li, Chen, Dan Xue, Fanjie Kong, Zhijie Hu, Hao Chen, Yudong Yao, Hongzan Sun, et al. "Cervical Histopathology Image Classification Using Ensembled Transfer Learning." In Advances in Intelligent Systems and Computing, 26–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23762-2_3.

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8

Ahmed, Hamza Kamel, Baraa Tantawi, Malak Magdy, and Gehad Ismail Sayed. "Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis." In Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023, 348–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43247-7_31.

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9

Tan, Jing Wei, and Won-Ki Jeong. "Histopathology Image Classification Using Deep Manifold Contrastive Learning." In Lecture Notes in Computer Science, 683–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_66.

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Roy, Bijoyeta, and Mousumi Gupta. "Macroscopic Reconstruction for Histopathology Images: A Survey." In Computer Vision and Machine Intelligence in Medical Image Analysis, 101–12. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_11.

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Тези доповідей конференцій з теми "HISTOPATHOLOGY IMAGE"

1

Mannam, Varun, Yide Zhang, Yinhao Zhu, and Scott Howard. "Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.3.

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2

Tsai, Sheng-Ting, Chin-Cheng Chan, Homer H. Chen, Jeng-Wei Tjiu, and Sheng-Lung Huang. "Segmentation based OCT Image to H&E-like Image Conversion." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.5.

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3

Sugie, Kenji, Kiyotaka Sasagawa, Mark Christian Guinto, Makito Haruta, Takashi Tokuda, and Jun Ohta. "Image refocusing of miniature CMOS image sensor with angle-selective pixels." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mth3a.5.

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4

Rueden, Curtis T., and Kevin Eliceiri. "The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/microscopy.2018.mth2a.3.

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Li, Xinyang, Zhifeng Zhao, Guoxun Zhang, Hui Qiao, Haoqian Wang, and Qinghai Dai. "High-fidelity fluorescence image restoration using deep unsupervised learning." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.2.

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6

Wang, Hongda, Yair Rivenson, Yiyin Jin, Zhensong Wei, Ronald Gao, Harun Günaydın, Laurent A. Bentolila, Comert Kural, and Aydogan Ozcan. "Deep learning-based super-resolution and image transformation into structured illumination microscopy." In Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.4.

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Sikaroudi, Milad, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh. "Magnification Generalization For Histopathology Image Embedding." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433978.

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Hou, Le, Kunal Singh, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Roberta J. Seidman, and Joel H. Saltz. "Automatic histopathology image analysis with CNNs." In 2016 New York Scientific Data Summit (NYSDS). IEEE, 2016. http://dx.doi.org/10.1109/nysds.2016.7747812.

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9

"Customized EfficientNet for Histopathology Image Representation." In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. http://dx.doi.org/10.1109/ssci51031.2022.10022191.

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T, Soumya. "Detection and Differentiation of blood cancer cells using Edge Detection method." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/zbua6077/ngcesi23p138.

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Анотація:
Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption
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