Relevant bibliographies by topics / Whole slide image classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Whole slide image classification'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Whole slide image classification"

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Liu, Dehua, Chengming Li, Xiping Hu, and Bin Hu. "Dual-Attention Multiple Instance Learning Framework for Pathology Whole-Slide Image Classification." Electronics 13, no. 22 (2024): 4445. http://dx.doi.org/10.3390/electronics13224445.

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Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are high-resolution tissue images that have made significant contributions to cancer diagnosis and prognosis assessment. Due to the complexity of WSIs and the availability of only slide-level labels, multiple instance learning (MIL) has become the primary framework for WSI classification. However, most MIL methods fail to capture the interdependence amo
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Fridman, M. V., A. A. Kosareva, E. V. Snezhko, P. V. Kamlach, and V. A. Kovalev. "Papillary thyroid carcinoma whole-slide images as a basis for deep learning." Informatics 20, no. 2 (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
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Feng, Ming, Kele Xu, Nanhui Wu, et al. "Trusted multi-scale classification framework for whole slide image." Biomedical Signal Processing and Control 89 (March 2024): 105790. http://dx.doi.org/10.1016/j.bspc.2023.105790.

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Zarella, Mark D., Matthew R. Quaschnick;, David E. Breen, and Fernando U. Garcia. "Estimation of Fine-Scale Histologic Features at Low Magnification." Archives of Pathology & Laboratory Medicine 142, no. 11 (2018): 1394–402. http://dx.doi.org/10.5858/arpa.2017-0380-oa.

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Context.— Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures—whether computational or visual—often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether
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Chen, Kaitao, Shiliang Sun, and Jing Zhao. "CaMIL: Causal Multiple Instance Learning for Whole Slide Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (2024): 1120–28. http://dx.doi.org/10.1609/aaai.v38i2.27873.

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Whole slide image (WSI) classification is a crucial component in automated pathology analysis. Due to the inherent challenges of high-resolution WSIs and the absence of patch-level labels, most of the proposed methods follow the multiple instance learning (MIL) formulation. While MIL has been equipped with excellent instance feature extractors and aggregators, it is prone to learn spurious associations that undermine the performance of the model. For example, relying solely on color features may lead to erroneous diagnoses due to spurious associations between the disease and the color of patch
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Yang, Xinda, Ranze Zhang, Yuan Yang, Yu Zhang, and Kai Chen. "PathEX: Make good choice for whole slide image extraction." PLOS ONE 19, no. 8 (2024): e0304702. http://dx.doi.org/10.1371/journal.pone.0304702.

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Background The tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions. Methods We developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification
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Lewis, Joshua, Conrad Shebelut, Bradley Drumheller, et al. "An Automated Pipeline for Cell Differentials on Whole-Slide Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 158, Supplement_1 (2022): S12. http://dx.doi.org/10.1093/ajcp/aqac126.020.

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Abstract Current pathologic diagnosis of benign and neoplastic bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of nucleated cell populations to obtain a cell differential. This manual process has significant limitations, including the limited sample of cells analyzed by a conventional 500-cell differential compared to the thousands of nucleated cells present, as well as the inter-observer variability seen between differentials on single samples due to differences in cell selection and classification. To address these sho
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Cui, Yi, Yao Li, Jayson R. Miedema, et al. "Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images—Nevus and Melanoma." Cancers 16, no. 15 (2024): 2616. http://dx.doi.org/10.3390/cancers16152616.

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Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort which contains 160 hematoxylin and eosin whole slide images of primary melanoma (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep learning method to allow for classification, at the slide
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Ahmed, Shakil, Asadullah Shaikh, Hani Alshahrani, et al. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images." Sensors 21, no. 16 (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 h
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Franklin, Daniel L., Tara Pattilachan, and Anthony Magliocco. "Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet." Cancer Research 82, no. 12_Supplement (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 cl
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Dissertations / Theses on the topic "Whole slide image classification"

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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 segme
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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.
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Zaidi, Fatima. "Deep learning-based scale-invariant cancer detection from whole slide image." Thesis, Zaidi, Fatima (2021) Deep learning-based scale-invariant cancer detection from whole slide image. Masters by Research thesis, Murdoch University, 2021. https://researchrepository.murdoch.edu.au/id/eprint/63326/.

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Convential cancer diagnosis methods from whole slide images (WSI) train a deep Convolutional Neural Network (CNN) to make patch level predictions, and then aggregate the image-level predictions to classify a tumour as either benign or malignant. To classify a patch, the CNN extracts features through convolutional layers and then process the feature maps using fully connected layers. The size of the filters used in the convolutional layers defines the receptive field of the network. Small filters are computationally efficient but do not capture a large context. On the other hand, large filters
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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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Rydell, Christopher. "Deep Learning for Whole Slide Image Cytology : A Human-in-the-Loop Approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-450356.

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With cancer being one of the leading causes of death globally, and with oral cancers being among the most common types of cancer, it is of interest to conduct large-scale oral cancer screening among the general population. Deep Learning can be used to make this possible despite the medical expertise required for early detection of oral cancers. A bottleneck of Deep Learning is the large amount of data required to train a good model. This project investigates two topics: certainty calibration, which aims to make a machine learning model produce more reliable predictions, and Active Learning, wh
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Williams, Paul James. "Near infrared (NIR) hyperspectral imaging for evaluation of whole maize kernels: chemometrics for exploration and classification." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/1696.

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Thesis (Msc Food Sc (Food Science))--University of Stellenbosch, 2009.<br>The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between whole maize kernels of varying degrees of hardness and fungal infected and non-infected kernels have been investigated. Near infrared hyperspectral images of whole maize kernels of varying degrees of hardness were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm as well as a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral ran
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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.<br>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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Venâncio, Rui Miguel Morgado. "Micrometastasis detection guidance by whole-slide image texture analysis in colorectal lymph nodes correlated with QUS parameters." Master's thesis, 2016. http://hdl.handle.net/10316/32150.

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Dissertação de Mestrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.<br>O cancro ´e uma doen¸ca que afeta milh˜oes por todo o mundo e uma identifica¸c˜ao correta de gˆanglios linf´aticos pr´oximos do tumor prim´ario, que contenham regi˜oes metast´aticas ´e de extrema importˆancia para um correto gerenciamento dos pacientes. A avalia¸c˜ao histopatol´ogica ´e o ´unico m´etodo aceite para fazer essa identifica¸c˜ao. Novas t´ecnicas emergentes como os ultrassons quantitativos podem ajudar nessa identifica¸c˜ao, detetando regi˜oes metast´
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Rosenbloom, Raymond. "Multiplex immunohistochemical analysis of granulomatous inflammation in lung tissue sections using a mouse model of M. avium infection." Thesis, 2020. https://hdl.handle.net/2144/41719.

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INTRODUCTION: Investigating mechanisms of how intracellular bacterial pathogens such as Mycobacterium. avium (M. avium) evade the host immune response and replicate within macrophages is crucial to devising rational targets for host-directed therapies (HDT) against these associated diseases. This studied utilized the congenic mouse strain B6.Sst1S, which contains the super-susceptibility to tuberculosis (TB) allele. Among murine models of TB, this strain uniquely replicates human disease because mice develop granulomas with central caseous necrosis. Utilizing a susceptible model for M. avium i
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Donner, Ralf. "Die visuelle Interpretation von Fernerkundungsdaten." Doctoral thesis, 2007. https://tubaf.qucosa.de/id/qucosa%3A22626.

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Die Fähigkeit, in Luft- und Satellitenbildern Objekte wiederzuerkennen, kann folgendermaßen erklärt werden: Aus der Kenntnis einer Landschaft und ihrer Abbildung im Bild werden Interpretationsregeln entwickelt, die bestimmten Kombinationen von Bildmerkmalen wie Farbe, Form, Größe, Textur oder Kontext festgelegte Bedeutungen zuordnen. Kommt es nicht auf das Wiedererkennen mit festen Wahrnehmungsmustern an, stellt sich die bislang offene Frage nach einer wissenschaftlichen Kriterien genügenden Methode, wie der gedankliche Zusammenhang zwischen den Sinneswahrnehmungen erfasst werden kann. Die Erf
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Books on the topic "Whole slide image classification"

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Karapapa, Stavroula. Defences to Copyright Infringement. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198795636.001.0001.

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Defences to copyright infringement have gained increased significance over the past twenty years. The fourth industrial revolution emerged with the development of innovative copy-reliant services and business models, transforming the way in which copyright works can be used, from digital learning methods to mass digitization initiatives, media monitoring services, image transformation tools, and content mining technologies. The lawfulness of such innovative services and business methods, which arguably have the potential to enhance public welfare, is dubious and challenges copyright law. EU co
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Book chapters on the topic "Whole slide image classification"

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Rymarczyk, Dawid, Adam Pardyl, Jarosław Kraus, Aneta Kaczyńska, Marek Skomorowski, and Bartosz Zieliński. "ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification." In Machine Learning and Knowledge Discovery in Databases. 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 intr
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Ding, Saisai, Jun Wang, Juncheng Li, and Jun Shi. "Multi-scale Prototypical Transformer for Whole Slide Image Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_58.

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Li, Jiahui, Wen Chen, Xiaodi Huang, et al. "Hybrid Supervision Learning for Pathology Whole Slide Image Classification." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_30.

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Huang, Wentao, Xiaoling Hu, Shahira Abousamra, Prateek Prasanna, and Chao Chen. "Hard Negative Sample Mining for Whole Slide Image Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72083-3_14.

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

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Zhang, Yunlong, Honglin Li, Yunxuan Sun, Sunyi Zheng, Chenglu Zhu, and Lin Yang. "Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73668-1_8.

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Gavade, Anil B., Rajendra B. Nerli, Shridhar Ghagane, Priyanka A. Gavade, and Venkata Siva Prasad Bhagavatula. "Cancer Cell Detection and Classification from Digital Whole Slide Image." In Smart Technologies in Data Science and Communication. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6880-8_31.

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

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Liang, Peixian, Hao Zheng, Hongming Li, Yuxin Gong, Spyridon Bakas, and Yong Fan. "Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72083-3_10.

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Xiong, Conghao, Yi Lin, Hao Chen, et al. "TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72083-3_47.

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Conference papers on the topic "Whole slide image classification"

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Gupta, Ravi Kant, Dadi Dharani, Shambhavi Shanker, and Amit Sethi. "Efficient Whole Slide Image Classification Through Fisher Vector Representation." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820480.

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Cugmas, Blaž, Eva Štruc, Mindaugas Tamosiunas, et al. "Comparison of two fixation methods in automated pollen classification on whole slide images." In Latin America Optics and Photonics Conference. Optica Publishing Group, 2024. https://doi.org/10.1364/laop.2024.w4a.31.

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We compared automated MobileNet V3 Large-based pollen classification accuracy on whole slide images. Pollen fixation to microscope slides with silicone achieved higher median accuracy (78.7%) than the standard adhesive tape-based fixation (68.9%).
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Wu, Zhifeng, Xiaohui Li, Luning Wang, Shendi Wang, Yufei Cui, and Jiahai Wang. "ProtoTree-MIL: Interpretable Multiple Instance Learning for Whole Slide Image Classification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650015.

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Du, Xianglong, JiaQi Guo, Zehang Xing, et al. "Hard example mining in Multi-Instance Learning for Whole-Slide Image Classification." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782609.

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Zheng, Tingting, Kui Jiang, and Hongxun Yao. "Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00767.

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Wan, Jiayu, Yingguang Hao, Xiaorui Ma, and Hongyu Wang. "Multi-Scale Graph-Based Cross-Attention Transformer for Whole Slide Image Classification." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10918883.

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Ahuja, Anu, Arthur Morley-Bunker, and Ramakrishnan Mukundan. "Deep Learning Classification of Microsatellite Status in Colorectal Cancer Whole Slide Images." In 2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2024. https://doi.org/10.1109/ivcnz64857.2024.10794480.

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Raswa, Farchan Hakim, Chun-Shien Lu, and Jia-Ching Wang. "Knowledge Sharing via Mimicking Attention Guided-Discriminative Features in Whole Slide Image Classification." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880812.

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Yu, Hongxuan, Jiayi Wu, Jichen Xu, et al. "RCNet: A Redundant Compression Network Using Information Bottleneck for Pathology Whole Slide Image Classification." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822836.

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Gupta, Ravi Kant, Shounak Das, and Amit Sethi. "Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820477.

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