Bibliografie tematiche / Whole slide images classification

Letteratura scientifica selezionata sul tema "Whole slide images classification"

Autore: Grafiati
Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Whole slide images classification":

1

Fell, Christina, Mahnaz Mohammadi, David Morrison, Ognjen Arandjelović, Sheeba Syed, Prakash Konanahalli, Sarah Bell, Gareth Bryson, David J. Harrison e David Harris-Birtill. "Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence". PLOS ONE 18, n. 3 (8 marzo 2023): e0282577. http://dx.doi.org/10.1371/journal.pone.0282577.

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In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with “malignant” and “other or benign” areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being “malignant” or “other or benign”. Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either “malignant”, “other or benign” or “insufficient”. The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists’ workload.
2

Govind, Darshana, Brendon Lutnick, John E. Tomaszewski e Pinaki Sarder. "Automated erythrocyte detection and classification from whole slide images". Journal of Medical Imaging 5, n. 02 (10 aprile 2018): 1. http://dx.doi.org/10.1117/1.jmi.5.2.027501.

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Neto, Pedro C., Sara P. Oliveira, Diana Montezuma, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto e Jaime S. Cardoso. "iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images". Cancers 14, n. 10 (18 maggio 2022): 2489. http://dx.doi.org/10.3390/cancers14102489.

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Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
4

Franklin, Daniel L., Tara Pattilachan e Anthony Magliocco. "Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet". Cancer Research 82, n. 12_Supplement (15 giugno 2022): 5048. http://dx.doi.org/10.1158/1538-7445.am2022-5048.

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Abstract This study aimed to determine whether EfficientNet-B0 was able to classify EGFR mutation subtypes with H&E stained whole slide images of lung and lymph node tissue. Background: Non-small cell lung cancer (NSCLC) accounts for the majority of all lung adenocarcinomas, with estimates that up to a third of such cases have a mutation in their epidermal growth factor receptor (EGFR). EGFR mutations can occur in various subtypes, such as Exon19 deletion, and L858R substitution, which are important for early therapy decisions. Here, we propose a deep learning approach for detecting and classifying EGFR mutation subtypes, which will greatly reduce the cost of determining mutation status, allowing for testing in a low resource setting. Methods: An EfficientNet-B0 model was trained with whole slide images of lung tissue or metastatic lymph nodes with known EGFR mutation subtype (wild type, exon19 deletion or L858R substitution). Regions of interest were tiled into 512x512 pixel images. The RGB .jpeg tiles are augmented by rotating 90°, 180°, 270°, and mirroring. The model was initialized with random parameters and trained with a batch size of 32, a learning rate of 0.0001 for 1 epoch before the validation loss increased for the next 5 epochs. Results: The model achieved a slide AUC of 0.8333, and a tile AUC of 0.8010. Slide AUC is the result of averaging all tiles within a slide and measuring performance based on correctly predicted slides (n=18). Tile AUC is the result of measuring performance based on correctly predicted tiles (n=102,000). Conclusion: Using EfficientNet-B0 architecture as the basis for our EGFR mutation classification system, we were able to create a top performing model and achieve a slide AUC of 0.833 and tile AUC of 0.801. Healthcare providers and researchers may utilize this AI model in clinical settings to allow for detection of EGFR mutation from routinely captured images and bypass expensive and time consuming sequencing methods. Table 1. Number of image tiles used and the number of slides they were extracted from. Train Validation Test Exon19 tiles 187,384 47,904 33,096 L858R tiles 166,288 19,512 26,136 Wild type tiles 225,944 27,696 42,768 Exon19 slides 47 6 6 L858R slides 46 6 6 WIld type slides 43 6 6 Citation Format: Daniel L. Franklin, Tara Pattilachan, Anthony Magliocco. Imaging based EGFR mutation subtype classification using EfficientNet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5048.
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Ahmed, Shakil, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber e Maheen Bakhtyar. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images". Sensors 21, n. 16 (9 agosto 2021): 5361. http://dx.doi.org/10.3390/s21165361.

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The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79.
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Fu, Zhibing, Qingkui Chen, Mingming Wang e Chen Huang. "Whole slide images classification model based on self-learning sampling". Biomedical Signal Processing and Control 90 (aprile 2024): 105826. http://dx.doi.org/10.1016/j.bspc.2023.105826.

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Fridman, M. V., A. A. Kosareva, E. V. Snezhko, P. V. Kamlach e V. A. Kovalev. "Papillary thyroid carcinoma whole-slide images as a basis for deep learning". Informatics 20, n. 2 (29 giugno 2023): 28–38. http://dx.doi.org/10.37661/1816-0301-2023-20-2-28-38.

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Objectives. Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a system of similar anatomical region in the histopathological image. Authors discuss the second selection of signs for the marking of histological images, methodological approaches to dissect whole-slide images, how to prepare raw data for a future analysis. The influence of the representative size of the fragment of the full-to-suction image of papillary thyroid cancer on the accuracy of the classification of trained neural network EfficientNetB0 is conducted. The analysis of the resulting results is carried out, the weaknesses of the use of fragments of images of different representative size and the cause of the unsatisfactory accuracy of the classification on large increase are evaluated.Materials and methods. Histopathological whole-slide imaged of 129 patients were used. Histological micropreparations containing elements of a tumor and surrounding tissue were scanned in the Aperio AT2 (Leica Biosystems, Germany) apparatus with maximum resolution. The marking was carried out in the ASAP software package. To choose the optimal representative size of the fragment the problem of classification was solved using the pre-study neural network EfficientNetB0.Results. A methodology for preparing a database of histopathological images of papillary thyroid cancer was proposed. Experiments were conducted to determine the optimal representative size of the image fragment. The best result of the accuracy of determining the class of test sample showed the size of a representative fragment as 394.32×394.32 microns.Conclusion. The analysis of the influence of the representative sizes of fragments of histopathological images showed the problems in solving the classification tasks because of cutting and staining images specifics, morphological complex and textured differences in the images of the same class. At the same time, it was determined that the task of preparing a set of data for training neural network to solve the problem of finding invasion of vessels in a histopathological image is not trivial and it requires additional stages of data preparation.
8

Jansen, Philipp, Adelaida Creosteanu, Viktor Matyas, Amrei Dilling, Ana Pina, Andrea Saggini, Tobias Schimming et al. "Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images". Journal of Fungi 8, n. 9 (28 agosto 2022): 912. http://dx.doi.org/10.3390/jof8090912.

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Background: Onychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists. Methods: In total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides. Results: The U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%). Conclusions: Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.
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Lewis, Joshua, Xuebao Zhang, Nithya Shanmugam, Bradley Drumheller, Conrad Shebelut, Geoffrey Smith, Lee Cooper e David Jaye. "Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears". American Journal of Clinical Pathology 156, Supplement_1 (1 ottobre 2021): S1—S2. http://dx.doi.org/10.1093/ajcp/aqab189.001.

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Abstract Manual microscopic examination of bone marrow aspirate (BMA) smears and counting of cell populations remains the standard of practice for accurate assessment of benign and neoplastic bone marrow disorders. While automated cell classification software using machine learning models has been developed and applied to BMAs, current systems nonetheless require manual identification of optimal regions within the slide that are rich in marrow hematopoietic cells. To address this issue, we have developed a machine learning-based platform for automated identification of optimal regions in whole-slide images of BMA smears. A training dataset was developed by manual annotation of 53 BMA slides across biopsy diagnoses including unremarkable trilineal hematopoiesis, acute leukemia, and plasma cell neoplasms, as well as across differences in total cellularity represented by a spectrum of marrow nucleated cell content and white blood cell counts. 10,537 regions among these 53 slides were manually annotated as either “optimal” (regions near aspirate particles with high proportions of marrow nucleated cells), “particle” (aspirate particles), or “hemodilute” (blood-rich regions with high proportions of red blood cells). Training of a neural network-based classifier on 10x magnification slides with region cropping and image augmentation resulted in a classifier with substantial accuracy on new testing-set BMA slides (one-vs-rest AUROC > 0.999 across 10 training/testing splits for all 3 region classes), with very few particle and hemodilute regions being classified as optimal (particle: 0.83%, hemodilute: 0.39%). Additionally, this classifier accurately classifies BMA regions on slides from hematological disorders not represented in the training data, including Burkitt lymphoma (AUROC > 0.999 across region classes), chronic myeloid leukemia (AUROC > 0.999 across region classes), and diffuse large B-cell lymphoma (AUROC = 1 across region classes), demonstrating the broad applicability of our approach. To assess the performance of our classifier on whole-slide images, tiles from 10x magnification slides were manually annotated by three participants with notable concordance (Krippendorff’s alpha = 0.424); substantial agreement was found between manual annotations and model predictions within whole-slide images (optimal AUROC = 0.958, particle AUROC = 1.0, hemodilute AUROC = 0.947). Based on these promising results, this machine learning-based region classification model is being connected to a previously-developed bone marrow cell classifier to fully automate differential cell counting in whole-slide images. The development of this novel automated pipeline has potential to streamline the diagnostic process for hematological disorders while enhancing accuracy and replicability, as well as decreasing diagnostic turnaround time for improving patient care.
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El-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed M. Soliman e Sherif A. Sami. "Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN". IEEE Access 9 (2021): 88429–38. http://dx.doi.org/10.1109/access.2021.3076158.

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Articoli di riviste

Tesi sul tema "Whole slide images classification":

1

Pirovano, Antoine. "Computer-aided diagnosis methods for cervical cancer screening on liquid-based Pap smears using convolutional neural networks : design, optimization and interpretability". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT011.

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Le cancer du col de l’utérus est le deuxième cancer le plus important pour les femmes après le cancer du sein. En 2012, le nombre de cas recensés dépasse 500,000 à travers le monde, dont la moitié se sont révélés mortels. Jusqu'à maintenant, le dépistage primaire du cancer du col de l’utérus est réalisé par l’inspection visuelle de cellules, prélevées par frottis vaginal, par des cytopathologistes utilisant la microscopie en fond clair dans des laboratoires de pathologie. En France, environ 5 millions de dépistage sont réalisés chaque année et environ 90% mènent à un diagnostic négatifs (i.e. pas de changements précancereux détectés). Pourtant, ces analyses au microscope sont extrêmement fastidieuses et coûteuses en temps pour le cytotechniciens et peut nécessiter l’avis conjoint de plusieurs experts. Ce processus impacte la capacité à traiter cette immense quantité de cas et à éviter les faux négatifs qui sont la cause principale des retards de traitements médicaux. Le manque d’automatisation et de traçabilité des dépistage deviennent ainsi de plus en plus critique à mesure que le nombre d’experts diminue. En ce sens, l’intégration d’outils numériques dans les laboratoires de pathologie devient une réelle problématique de santé publique et la voie privilégiée pour l’amélioration de ces laboratoires. Depuis 2012, l’apprentissage profond a révolutionné le domaine de la vision par ordinateur, en particulier grâce aux réseaux de neurones convolutionnels qui se sont montrés fructueux sur un large panel d’applications parmi lesquelles plusieurs en imagerie biomédicale. Parallèlement, le processus de digitalisation de lames entières a ouvert l’opportunité pour de nouveaux outils et de nouvelles méthodes de diagnostic assisté par ordinateur. Dans cette thèse, après avoir motivé le besoin médical et introduit l’état de l’art en terme de méthodes d’apprentissage profond pour le traitement de l’image, nous présentons nos contributions au domaine de la vision par ordinateur traitant le dépistage du cancer du col de l’utérus dans un contexte de cytologie en milieu liquide. Notre première contribution consiste à proposer une méthode simple de régularisation pour l’entrainement de modèles dans le contexte d’une classification ordinale (i.e. classes suivant un ordre). Nous démontrons l’avantage de notre méthode pour la classification de cellules utérines en utilisant sur le jeu de données Herlev. De plus, nous proposons de nous appuyer sur des explications basées sur le gradient pour réaliser une localisation faiblement supervisée et plus généralement une détection d’anormalité. Finalement, nous montrons comment nous intégrons ces méthodes pour créer un outil assisté par ordinateur qui pourrait être utilisé afin de réduire la charge de travail des cytopathologistes. La seconde contribution se concentre sur la classification de lames entières et l’interprétabilité de ces approches. Nous présentons en détails les méthodes de classification de lames entières s’appuyant sur l’apprentissage multi-instances, et améliorons l’interprétabilité dans un contexte d’apprentissage faiblement supervisé via des visualizations de caractéristiques au niveau de la tuile et une nouvelle manière de calculer des cartes de chaleur explicatives. Finalement, nous appliquons ces méthodes pour le dépistage du cancer du col de l’utérus en utilisant un detecteur d’ “anormalité” qui guide l’entrainement pour l’échantillonnages de régions d’intérêt
Cervical cancer is the second most important cancer for women after breast cancer. In 2012, the number of cases exceeded 500,000 worldwide, among which half turned to be deadly.Until today, primary cervical cancer screening is performed by a regular visual analysis of cells, sampled by pap-smear by cytopathologists under brightfield microscopy in pathology laboratories. In France, about 5 millions of cervical screening are performed each year and about 90% lead to a negative diagnosis (i.e. no pre-cancerous changes detected). Yet, these analyses under microscope are extremely tedious and time-consuming for cytotechnicians and can require the joint opinion of several experts. This process has an impact on the capacity to tackle this huge amount of cases and to avoid false negatives that are the main cause of treatment delay. The lack of automation and traceability of screening is thus becoming more critical as the number of cyto-pathologists decreases. In that respect, the integration of digital tools in pathology laboratories is becoming a real public health stake for patients and the privileged path for the improvement of these laboratories. Since 2012, deep learning methods have revolutionized the computer vision field, in particular thanks to convolutional neural networks that have been applied successfully to a wide range of applications among which biomedical imaging. Along with it, the whole slide imaging digitization process has opened the opportunity for new efficient computer-aided diagnosis methods and tools. In this thesis, after motivating the medical needs and introducing the state-of-the-art deep learning methods for image processing and understanding, we present our contribution to the field of computer vision tackling cervical cancer screening in the context of liquid-based cytology. Our first contribution consists in proposing a simple regularization constraint for classification model training in the context of ordinal regression tasks (i.e. ordered classes). We prove the advantage of our method on cervical cells classification using Herlev dataset. Furthermore, we propose to rely on explanations from gradient-based explanations to perform weakly-supervised localization and detection of abnormality. Finally, we show how we integrate these methods as a computer-aided tool that could be used to reduce the workload of cytopathologists.The second contribution focuses on whole slide classification and the interpretability of these pipelines. We present in detail the most popular approaches for whole slide classification relying on multiple instance learning, and improve the interpretability in a context of weakly-supervised learning through tile-level feature visualizations and a novel manner of computing explanations of heat-maps. Finally, we apply these methods for cervical cancer screening by using a weakly trained “abnormality” detector for region of interest sampling that guides the training
2

Lerousseau, Marvin. "Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG015.

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L’anatomopathologie est la discipline médicale responsable du diagnostic et de la caractérisation des maladies par inspection macroscopique, microscopique, moléculaire et immunologique des tissus. Les technologies modernes permettent de numériser des lames tissulaire en images numériques qui peuvent être traitées par l’intelligence artificielle pour démultiplier les capacités des pathologistes. Cette thèse a présenté plusieurs approches nouvelles et puissantes qui s’attaquent à la segmentation et à la classification pan-cancer des images de lames numériques. L’apprentissage de modèles de segmentation pour des lames numériques est compliqué à cause de difficultés d’obtention d’annotations qui découlent (i) d’une pénurie de pathologistes, (ii) d’un processus d’annotation ennuyeux, et (iii) de différences majeurs entre les annotations inter-pathologistes. Mon premier axe de travail a abordé la segmentation des tumeurs pan-cancéreuses en concevant deux nouvelles approches d’entraînement faiblement supervisé qui exploitent des annotations à l’échelle de la lame qui sont faciles et rapides à obtenir. En particulier, ma deuxième contribution à la segmentation était un algorithme générique et très puissant qui exploite les annotations de pourcentages de tumeur pour chaque lame, sans recourir à des annotations de pixels. De vastes expériences à grande échelle ont montré la supériorité de mes approches par rapport aux méthodes faiblement supervisées et supervisées pour la segmentation des tumeurs pan-cancer sur un ensemble de données de plus de 15 000 lames de tissus congelés. Mes résultats ont également démontré la robustesse de nos approches au bruit et aux biais systémiques dans les annotations. Les lames numériques sont difficiles à classer en raison de leurs tailles colossales, qui vont de millions de pixels à plusieurs milliards de pixels, avec un poids souvent supérieur à 500 mégaoctets. L’utilisation directe de la vision par ordinateur traditionnelle n’est donc pas possible, incitant l’utilisation de l’apprentissage par instances multiples, un paradigme d’apprentissage automatique consistant à assimiler une lame comme un ensemble de tuiles uniformément échantillonnés à partir de cette dernière. Jusqu’à mes travaux, la grande majorité des approches d’apprentissage à instances multiples considéraient les tuiles comme échantillonnées de manière indépendante et identique, c’est-à-dire qu’elles ne prenaient pas en compte la relation spatiale des tuiles extraites d’une image de lame numérique. Certaines approches ont exploité une telle interconnexion spatiale en tirant parti de modèles basés sur des graphes, bien que le véritable domaine des lames numériques soit spécifiquement le domaine de l’image qui est plus adapté aux réseaux de neurones convolutifs. J’ai conçu un cadre d’apprentissage à instances multiples puissant et modulaire qui exploite la relation spatiale des tuiles extraites d’une lame numérique en créant une carte clairsemée des projections multidimensionnelles de patches, qui est ensuite traitée en projection de lame numérique par un réseau convolutif à entrée clairsemée, avant d’être classée par un modèle générique de classification. J’ai effectué des expériences approfondies sur trois tâches de classification d’images de lames numériques, dont la tâche par excellence du cancérologue de soustypage des tumeurs, sur un ensemble de données de plus de 20 000 images de lames numériques provenant de données publiques. Les résultats ont mis en évidence la supériorité de mon approche vis-à-vis les méthodes d’apprentissage à instances multiples les plus répandues. De plus, alors que mes expériences n’ont étudié mon approche qu’avec des réseaux de neurones convolutifs à faible entrée avec deux couches convolutives, les résultats ont montré que mon approche fonctionne mieux à mesure que le nombre de paramètres augmente, suggérant que des réseaux de neurones convolutifs plus sophistiqués peuvent facilement obtenir des résultats su
Anatomic pathology is the medical discipline responsible for the diagnosis and characterization of diseases through the macroscopic, microscopic, molecular and immunologic inspection of tissues. Modern technologies have made possible the digitization of tissue glass slides into whole slide images, which can themselves be processed by artificial intelligence to enhance the capabilities of pathologists. This thesis presented several novel and powerful approaches that tackle pan-cancer segmentation and classification of whole slide images. Learning segmentation models for whole slide images is challenged by an annotation bottleneck which arises from (i) a shortage of pathologists, (ii) an intense cumbersomeness and boring annotation process, and (iii) major inter-annotators discrepancy. My first line of work tackled pan-cancer tumor segmentation by designing two novel state-of-the-art weakly supervised approaches that exploit slide-level annotations that are fast and easy to obtain. In particular, my second segmentation contribution was a generic and highly powerful algorithm that leverages percentage annotations on a slide basis, without needing any pixelbased annotation. Extensive large-scale experiments showed the superiority of my approaches over weakly supervised and supervised methods for pan-cancer tumor segmentation on a dataset of more than 15,000 unfiltered and extremely challenging whole slide images from snap-frozen tissues. My results indicated the robustness of my approaches to noise and systemic biases in annotations. Digital slides are difficult to classify due to their colossal sizes, which range from millions of pixels to billions of pixels, often weighing more than 500 megabytes. The straightforward use of traditional computer vision is therefore not possible, prompting the use of multiple instance learning, a machine learning paradigm consisting in assimilating a whole slide image as a set of patches uniformly sampled from it. Up to my works, the greater majority of multiple instance learning approaches considered patches as independently and identically sampled, i.e. discarded the spatial relationship of patches extracted from a whole slide image. Some approaches exploited such spatial interconnection by leveraging graph-based models, although the true domain of whole slide images is specifically the image domain which is more suited with convolutional neural networks. I designed a highly powerful and modular multiple instance learning framework that leverages the spatial relationship of patches extracted from a whole slide image by building a sparse map from the patches embeddings, which is then further processed into a whole slide image embedding by a sparse-input convolutional neural network, before being classified by a generic classifier model. My framework essentially bridges the gap between multiple instance learning, and fully convolutional classification. I performed extensive experiments on three whole slide image classification tasks, including the golden task of cancer pathologist of subtyping tumors, on a dataset of more than 20,000 whole slide images from public data. Results highlighted the superiority of my approach over all other widespread multiple instance learning methods. Furthermore, while my experiments only investigated my approach with sparse-input convolutional neural networks with two convolutional layers, the results showed that my framework works better as the number of parameters increases, suggesting that more sophisticated convolutional neural networks can easily obtain superior results
3

Дяченко, Є. В. "Інформаційна технологія розпізнавання онкопатологій на повнослайдових гістологічних зображеннях". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/78594.

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Виконано аналіз метаданих повнослайдових гістологічних зображень та отримано результати їх впливу на швидкодію і точність класифікаційного алгоритму. Розроблено програмний модуль онкодіагностування з використанням методу опорних векторів SVM та виконана його оптимізація, в результаті якої алгоритм здатен встановлювати вірний діагноз з точністю 95%. Розроблений модуль створено за допомогою мови програмування Python та імпортовано до WSI-системи QuPath.
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Phillips, Adon. "Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images". Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36929.

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Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastatic melanoma. This pipeline includes processes for annotating and preparing a dataset from the output of a tissue slide scanner to the patch-based training and inference by an artificial neural network. We have curated a large dataset of 50 whole slide images containing cutaneous melanoma or cutaneous metastatic melanoma that are fully annotated at 40× ob- jective resolution by an expert pathologist. We will publish the source images of this dataset online. We also present two new FCN architectures that fuse multiple deconvolutional strides, combining coarse and fine predictions to improve accuracy over similar networks without multi-stride information. Our results show that the system performs better than our comparators. We include inference results on thousands of patches from four whole slide images, reassembling them into whole slide segmentation masks to demonstrate how our system generalizes on novel cases.
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Trahearn, Nicholas. "Registration and multi-immunohistochemical analysis of whole slide images of serial tissue sections". Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/89986/.

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The identification and classification of tissue abnormalities for the purpose of disease diagnosis have been greatly served by the discipline of histopathology, and Immunohistochemistry (IHC) in particular. The advent of digital slide scanners and computerised slide viewing software have opened the door for introducing automated algorithms into what has traditionally been a predominantly manual discipline. Multi-IHC analysis is one potential area of interest for automation, which will be discussed in detail in this work. Analysis occurs on serial sections of tissue, which must be realigned before their IHC marker expressions can be compared directly. This requires a robust method of serial section registration. Two methods of automated serial section registration are present, which are each designed to align a particular tissue type: breast core biopsy sections or resected colorectal cancer sections. Automated multi-IHC analysis is presented from the perspective of two case studies: Scoring of Oestrogen Receptor and Progesterone Receptor (ER/PR) on breast core biopsies and IHC scoring and colocalisation of resected colorectal cancer (CRC) sections. For each case study the background of the problem is introduced, followed by a discussion of how each type of analysis is performed in clinical practice, and it is then explained how this is implemented as an automated algorithm. For the scoring of ER/PR, it is shown that the algorithm can achieve good agreement with a pathologist on a sample of 50 cases, which suggests that automated ER/PR scoring is suitable for clinical practice. For the analysis of CRC, the results of scoring and colocalisation are shown in the form of localised maps with a discussion into how they may be used for further analysis. As part of this framework a number of additional steps must be carried out before the goal of multi-IHC analysis can be realised. Two pre-processing steps, both of which are key to ensuring that the end results are of the highest quality, are presented: Tissue Segmentation and Out of Focus Area detection. A complete Out of Focus Area detection system is presented, which has led to the development of a Windows software that is currently being used in a local hospital. In addition, we present an automated method of Stain Separation, based around Independent Component Analysis, which allows us to extract and process the IHC marker expressions directly. This method includes a novel correction process to improve any faults in the primary analysis.
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Khire, Sourabh Mohan. "Time-sensitive communication of digital images, with applications in telepathology". Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29761.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Jayant, Nikil; Committee Member: Anderson, David; Committee Member: Lee, Chin-Hui. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Huang, Pei-Chen, e 黃珮楨. "Real Time Automatic Lung Tumor Segmentation in Whole-slide Histopathological Images". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2h8u6r.

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Lee, Chieh-Chi, e 李捷琦. "Computer-aided diagnosis of mycobacteria bacilli detection in digital whole slide pathological images with deep learning architecture". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/738y92.

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Münch, Benno Jürgen Helmut. "Whole Tumor Histogramm-profiling of Diffusion-Weighted Magnetic Resonance Images reflects tumorbiological features of Primary Central Nervous System Lymphoma". 2018. https://ul.qucosa.de/id/qucosa%3A34107.

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Das Ziel der vorliegenden Arbeit war die Untersuchung des Zusammenhangs zwischen Parametern in der bildgebenden Diagnostik mittels Diffusion-Weighted Imaging und histopathologischen Eigenschaften von primären Lymphomen des zentralen Nervensystems. Hierzu wurden die bioptischen Resektate von 21 Patient*innen mit der gesicherten Diagnose eines primären Lymphoms des zentralen Nervensystems neuropathologisch untersucht. Es wurden der Ki-67 Index, die Zellzahl und das durchschnittliche sowie gesamte Zellkern-Areal bestimmt. In der bildgebenden Diagnostik erfolgte die Untersuchung der MRT Bildgebung der Patient*innen. Die MRT Daten wurden vor der neurochirurgisch durchgeführten bioptischen Sicherung der Läsionen erhoben und beinhalteten mindestens eine Diffusions-gewichtete Aufnahme, ein ADC Mapping, eine T1 Sequenz vor und nach Kontrastmittelgabe sowie eine T2_tirm_tra_dark_fluid Sequenz. Es erfolgte dann eine Markierung der kontrastmittelaufnehmenden Läsionen in der T1 Sequenz nach Kontrastmittelgabe in jeder axialen Schicht, welche von der Raumforderung betroffen war. Bei multilokulären Befunden wurde die Läsion ausgewählt, welche schlußendlich neurochirurgisch bioptiert wurde. Mittels eines DICOM Image Analysis Programms auf der Basis von Matlab wurden dann die getätigten Markierungen auf die korrespondierenden ADC-Maps übertragen, sodass schlußendliche eine diffusions-gewichtete MRT Untersuchung des Gesamt-Tumors ermöglicht wurde. In dem berechneten Histogram Datensatz wurde folgende Parameter einbezogen: ADC mean, ADC min, ADC max, ADC p10, ADC p25, ADC p75, ADC p90, ADCmodus, ADCmedian, Skewness, Kurtosis und Entropy. Hierauf erfolgte die statistische Aufarbeitung der Daten und Untersuchung auf vorhandene Korrelationen mittels des Analyse-Programms SPSS 23.0. Dazu erfolgte zunächst die beschreibende Analyse der Daten mittels Mittelwert, Standardabweichung und Feststellung von Minimum und Maximum. Sodann erfolgte die Untersuchung der Daten bezüglich einer Gauß-/Normal- Verteilung mittels des Shapiro-Wilk-Tests. Schlußendlich erfolgte dann eine Untersuchung der Korrelationen mittels des Pearson Korrelationsskoeffizienten für die normalverteilten Daten sowie mittels Spearman-Rho Rank-Order Korrelation für die nicht normalverteilten Daten. Ein statistisches Signifikanzniveau von P<0,05 wurde festgelegt. Es lässt sich zusammenfassend bezüglich der Ergebnisse darstellen, dass verschiedene statistisch signifikante Korrelationen zwischen Parametern der diffusions-gewichteten Bildgebung sowie histopathologischer Parameter bei primären Lymphomen des zentralen Nervensystems gefunden wurden. Im Detail konnte eine statistisch signifikante Korrelation zwischen der Kurtosis des ADC-Histograms und der histopathologischen Zellzahl gezeigt werden, zwischen den niedrigeren Perzentilen der ADC Werte (p10, p25) und der Zellzahl sowie der Zellkern beinhaltenden Areale und eine signifikante Korrelation zwischen dem ADCmean Wert und dem Ki-67 Index sowie des gesamten Zellkern beinhaltenden Areals. Zudem wurde eine inverse Korrelation zwischen der 90. ADC Perzentile und dem Ki-67 Index festgestellt. Hieraus sind verschiedene Schlussfolgerungen möglich. Zum einen zeigt die diffusions-gewichtete Untersuchung des gesamten Tumors Korrelationen mit Parametern der histopathologischen Differenzierung, welche sich im klinischen Verlauf bei primären Lymphomen des zentralen Nervensystems widerspiegeln. Die Technik ist also eine gute Ergänzung der bereits beschriebenen Differenzierung mittels diffusionsgewichteter singulärer Region of Interest Untersuchung. Somit können bereits vor neurochirurgischer Sicherung der Läsionen und somit vor Beginn einer Behandlung Aussagen über die wahrscheinliche Tumorarchitektur bzw. histopathologische Eigenschaften getroffen werden. Zum anderen scheint diese Technik im Rahmen weiterer Studien und Forschungen ein verheißungsvoller Kandidat für die Entwicklung bildgebender Marker zur Kontrolle beispielsweise des Tumorverhaltens bei medikamentöser Therapie zu sein. Außerdem ist es denkbar, die Ergebnisse der Untersuchungen bei primären Lymphomen des zentralen Nervensystems auf andere Tumorentitäten zu erweitern, und somit die Vorteile dieser nicht invasiven und leicht verfügbaren Methode bei verschiedenen Krankheitsbildern anzuwenden. Für diese Weiterentwicklung der Technik bedarf es jedoch noch ausführlicher Studien, insbesondere die Vergrößerung der Patient*innenkollektive, eine Vergleichbarkeit zwischen verschiedenen technischen Systemen und Untersuchern und prospektive Studiendesigns sind noch zu erreichende Ziele.

Capitoli di libri sul tema "Whole slide images classification":

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Apou, Grégory, Benoît Naegel, Germain Forestier, Friedrich Feuerhake e Cédric Wemmert. "Efficient Region-based Classification for Whole Slide Images". In Communications in Computer and Information Science, 239–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25117-2_15.

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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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Kwok, Scotty. "Multiclass Classification of Breast Cancer in Whole-Slide Images". In Lecture Notes in Computer Science, 931–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_106.

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Zhang, Jingwei, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou e Dimitris Samaras. "Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning". In Lecture Notes in Computer Science, 192–201. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_19.

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Gadermayr, Michael, Martin Strauch, Barbara Mara Klinkhammer, Sonja Djudjaj, Peter Boor e Dorit Merhof. "Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images". In Lecture Notes in Computer Science, 616–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_69.

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Ren, Jian, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran e Xin Qi. "Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 201–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_23.

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Li, Jiahui, Wen Chen, Xiaodi Huang, Shuang Yang, Zhiqiang Hu, Qi Duan, Dimitris N. Metaxas, Hongsheng Li e Shaoting Zhang. "Hybrid Supervision Learning for Pathology Whole Slide Image Classification". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 309–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_30.

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Ding, Saisai, Jun Wang, Juncheng Li e Jun Shi. "Multi-scale Prototypical Transformer for Whole Slide Image Classification". In Lecture Notes in Computer Science, 602–11. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_58.

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Shen, Yiqing, e Jing Ke. "A Deformable CRF Model for Histopathology Whole-Slide Image Classification". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 500–508. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_48.

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Zheng, Yushan, Jun Li, Jun Shi, Fengying Xie e Zhiguo Jiang. "Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification". In Lecture Notes in Computer Science, 283–92. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_28.

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Atti di convegni sul tema "Whole slide images classification":

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Khvostikov, Alexander, Andrey Krylov, Ilya Mikhailov, Pavel Malkov e Natalya Danilova. "Tissue Type Recognition in Whole Slide Histological Images". In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-496-507.

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Automatic layers recognition of the wall of the stomach and colon on whole slide images is an extremely urgent task in digital pathology as it can be used for automatic determining the depth of invasion of the digestive tract tumors. In this paper we propose a new CNN-based method of automatic tissue type recognition on whole slide histological images. We also describe an effective pipeline of training that uses 2 different training datasets. The proposed method of automatic tissue type recognition achieved 0.929 accuracy and 0.903 balanced accuracy on CRC-VAL-HE-7K dataset for 9-types classification and 0.98 accuracy and 0.926 balanced accuracy on the test subset of whole slide images from PATH-DT- MSU dataset for 5-types classification. The developed method makes it possible to classify the areas corresponding to the gastric own mucous glands in the lamina propria and also to distinguish the tubular structures of a highly differentiated gastric adenocarcinoma with normal glands.
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Bug, Daniel, Julia Schuler, Friedrich Feuerhake e Dorit Merhof. "Multi-class single-label classification of histopathological whole-slide images". In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493527.

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Yadav, Ankur, Ovidiu Daescu, Patrick Leavey e Erin Rudzinski. "Machine Learning for Rhabdomyosarcoma Whole Slide Images Sub-type Classification". In PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3594806.3594865.

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Tavolara, Thomas E., M. Khalid Khan Niazi e Metin N. Gurcan. "Background detection affects downstream classification of Camelyon16 whole slide images". In Digital and Computational Pathology, a cura di John E. Tomaszewski e Aaron D. Ward. SPIE, 2023. http://dx.doi.org/10.1117/12.2653882.

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El-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed Mostafa e Sherif A. Sami. "A robust CNN classification of whole slide thyroid carcinoma images". In 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, 2021. http://dx.doi.org/10.1109/jac-ecc54461.2021.9691433.

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van Zon, Mike, Nikolas Stathonikos, Willeke A. M. Blokx, Selim Komina, Sybren L. N. Maas, Josien P. W. Pluim, Paul J. van Diest e Mitko Veta. "Segmentation and Classification of Melanoma and Nevus in Whole Slide Images". In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098487.

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Gao, Zeyu, Anyu Mao, Jialun Wu, Yang Li, Chunbao Wang, Caixia Ding, Tieliang Gong e Chen Li. "Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images". In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995601.

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Zhang, Chaoyi, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen e Weidong Cai. "Whole Slide Image Classification via Iterative Patch Labelling". In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451551.

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Wetteland, Rune, Kjersti Engan, Trygve Eftestøl, Vebjørn Kvikstad e Emilius Janssen. "Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks". In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007253603200327.

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Alhammad, Sarah, Kun Zhao, Anthony Jennings, Peter Hobson, Daniel F. Smith, Brett Baker, Justin Staweno e Brian C. Lovell. "Efficient DNN-Based Classification of Whole Slide Gram Stain Images for Microbiology". In 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647415.

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