Relevant bibliographies by topics / Whole slide images classification

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

Academic literature on the topic 'Whole slide images classification'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Whole slide images classification"

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Fell, Christina, Mahnaz Mohammadi, David Morrison, et al. "Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence." PLOS ONE 18, no. 3 (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
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Govind, Darshana, Brendon Lutnick, John E. Tomaszewski, and Pinaki Sarder. "Automated erythrocyte detection and classification from whole slide images." Journal of Medical Imaging 5, no. 02 (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, et al. "iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images." Cancers 14, no. 10 (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 sensi
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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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Fu, Zhibing, Qingkui Chen, Mingming Wang, and Chen Huang. "Whole slide images classification model based on self-learning sampling." Biomedical Signal Processing and Control 90 (April 2024): 105826. http://dx.doi.org/10.1016/j.bspc.2023.105826.

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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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Jansen, Philipp, Adelaida Creosteanu, Viktor Matyas, et al. "Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images." Journal of Fungi 8, no. 9 (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
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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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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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Lewis, Joshua, Xuebao Zhang, Nithya Shanmugam, et al. "Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 156, Supplement_1 (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
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Dissertations / Theses on the topic "Whole slide images classification"

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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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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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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 metastati
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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 bef
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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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Huang, Pei-Chen, and 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, and 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 Bildge
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Book chapters on the topic "Whole slide images classification"

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Apou, Grégory, Benoît Naegel, Germain Forestier, Friedrich Feuerhake, and Cédric Wemmert. "Efficient Region-based Classification for Whole Slide Images." In Communications in Computer and Information Science. 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, 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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Kwok, Scotty. "Multiclass Classification of Breast Cancer in Whole-Slide Images." In Lecture Notes in Computer Science. 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, et al. "Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning." In Lecture Notes in Computer Science. 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, and Dorit Merhof. "Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images." In Lecture Notes in Computer Science. 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, and Xin Qi. "Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_23.

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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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Conference papers on the topic "Whole slide images classification"

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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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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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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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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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Al-Rubaian, Arwa, Gozde N. Gunesli, Wajd A. Althakfi, Ayesha Azam, Nasir Rajpoot, and Shan E. Ahmed Raza. "Cell Maps Representation for Lung Adenocarcinoma Growth Patterns Classification in Whole Slide Images." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635418.

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Innani, Shubham, Bhakti Baheti, MacLean P. Nasrallah, and Spyridon Bakas. "Weakly Supervised IDH-Status Glioma Classification from H&E-Stained Whole Slide Images." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635869.

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Hua, Yingchen, Bing Liu, Pengchao Lan, and Bei Yang. "DIMIL: data augmentation with the help of significant instances for whole slide images classification." In 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2025), edited by Peicheng Zhu and Guihua Lin. SPIE, 2025. https://doi.org/10.1117/12.3070447.

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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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Saeed, Ahmed, Nagia M. Ghanem, and Mohamed A. Ismail. "Efficient Strategy for Building Colorectal Cancer Classification CAD System Using Weakly-Annotated Whole Slide Images." In 2024 5th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, 2024. http://dx.doi.org/10.1109/airc61399.2024.10672475.

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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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