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Journal articles on the topic 'Whole slide image classification'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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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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Amgad, Mohamed, Habiba Elfandy, Hagar Hussein, et al. "Structured crowdsourcing enables convolutional segmentation of histology images." Bioinformatics 35, no. 18 (2019): 3461–67. http://dx.doi.org/10.1093/bioinformatics/btz083.

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Abstract Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-part

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Aftab, Rukhma, Yan Qiang, and Zhao Juanjuan. "Contrastive Learning for Whole Slide Image Representation: A Self-Supervised Approach in Digital Pathology." European Journal of Applied Science, Engineering and Technology 2, no. 2 (2024): 175–85. https://doi.org/10.59324/ejaset.2024.2(2).12.

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Image analysis in digital pathology is identified as a challenging field, particularly for AI-driven classification and search tasks. The high-resolution and large-scale nature of whole slide images (WSIs) present significant computational challenges in representing and analyzing these images effectively. The research endeavors to tackle these hurdles by presenting an innovative methodology grounded in self-supervised learning (SSL). Unlike prior SSL approaches that depend on augmenting at the patch level, the novel framework capitalizes on existing primary site information to directly glean e

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Oskouei, Soroush, André Pedersen, Marit Valla, et al. "OKEN: A Supervised Evolutionary Optimizable Dimensionality Reduction Framework for Whole Slide Image Classification." Bioengineering 12, no. 7 (2025): 733. https://doi.org/10.3390/bioengineering12070733.

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Classification of lung cancer subtypes is a critical clinical step; however, relying solely on H&E-stained histopathology images can pose challenges, and additional immunohistochemical analysis is sometimes required for definitive subtyping. Digital pathology facilitates the use of artificial intelligence for automatic classification of digital tissue slides. Automatic classification of Whole Slide Images (WSIs) typically involves extracting features from patches obtained from them. The aim of this study was to develop a WSI classification framework utilizing an optimizable kernel to encod

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Walkowski, Slawomir, and Janusz Szymas. "Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI)." Analytical Cellular Pathology 35, no. 2 (2012): 117–22. http://dx.doi.org/10.1155/2012/483525.

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Background: Making an automatic diagnosis based on virtual slides and whole slide imaging or even determining whether a case belongs to a single class, representing a specific disease, is a big challenge. In this work we focus on WHO Classification of Tumours of the Central Nervous System. We try to design a method which allows to automatically distinguish virtual slides which contain histopathologic patterns characteristic of glioblastoma – pseudopalisading necrosis and discriminate cases with neurinoma (schwannoma), which contain similar structures – palisading (Verocay bodies).Methods: Our

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Aftab, Rukhma, Yan Qiang, and Zhao Juanjuan. "Contrastive Learning for Whole Slide Image Representation: A Self-Supervised Approach in Digital Pathology." European Journal of Applied Science, Engineering and Technology 2, no. 2 (2024): 175–85. http://dx.doi.org/10.59324/ejaset.2024.2(2).12.

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Image analysis in digital pathology is identified as a challenging field, particularly for AI-driven classification and search tasks. The high-resolution and large-scale nature of whole slide images (WSIs) present significant computational challenges in representing and analyzing these images effectively. The research endeavors to tackle these hurdles by presenting an innovative methodology grounded in self-supervised learning (SSL). Unlike prior SSL approaches that depend on augmenting at the patch level, the novel framework capitalizes on existing primary site information to directly glean e

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Wang, Qian, Ying Zou, Jianxin Zhang, and Bin Liu. "Second-order multi-instance learning model for whole slide image classification." Physics in Medicine & Biology 66, no. 14 (2021): 145006. http://dx.doi.org/10.1088/1361-6560/ac0f30.

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Zhang, Jianan, Fang Hao, Xueyu Liu, et al. "Multi-scale multi-instance contrastive learning for whole slide image classification." Engineering Applications of Artificial Intelligence 138 (December 2024): 109300. http://dx.doi.org/10.1016/j.engappai.2024.109300.

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Molla, Md Rony, and Ma Jian Fen. "Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification." International Journal of Advanced Engineering Research and Science 10, no. 12 (2023): 096–104. http://dx.doi.org/10.22161/ijaers.1012.10.

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Multiple Instance Learning (MIL) is commonly utilized in weakly supervised whole slide image (WSI) classification. MIL techniques typically involve a feature embedding step using a pretrained feature extractor, then an aggregator that aggregates the embedded instances into predictions. Current efforts aim to enhance these sections by refining feature embeddings through self-supervised pretraining and modeling correlations between instances. In this paper, we propose a convolutional sparsely coded MIL (CSCMIL) that utilizes convolutional sparse dictionary learning to simultaneously address thes

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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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Balasubramanian, Aadhi Aadhavan, Salah Mohammed Awad Al-Heejawi, Akarsh Singh, et al. "Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology." Cancers 16, no. 12 (2024): 2222. http://dx.doi.org/10.3390/cancers16122222.

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Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching tec

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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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Ginley, Brandon, Brendon Lutnick, Kuang-Yu Jen, et al. "Computational Segmentation and Classification of Diabetic Glomerulosclerosis." Journal of the American Society of Nephrology 30, no. 10 (2019): 1953–67. http://dx.doi.org/10.1681/asn.2018121259.

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BackgroundPathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.MethodsWe developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally qu

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Rao, Roopa S., Divya B. Shivanna, Kirti S. Mahadevpur, et al. "Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies." Diagnostics 11, no. 12 (2021): 2184. http://dx.doi.org/10.3390/diagnostics11122184.

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Background: The goal of the study was to create a histopathology image classification automation system that could identify odontogenic keratocysts in hematoxylin and eosin-stained jaw cyst sections. Methods: From 54 odontogenic keratocysts, 23 dentigerous cysts, and 20 radicular cysts, about 2657 microscopic pictures with 400× magnification were obtained. The images were annotated by a pathologist and categorized into epithelium, cystic lumen, and stroma of keratocysts and non-keratocysts. Preprocessing was performed in two steps; the first is data augmentation, as the Deep Learning technique

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Dimitriou, Neofytos, Ognjen Arandjelović, and David J. Harrison. "Magnifying Networks for Histopathological Images with Billions of Pixels." Diagnostics 14, no. 5 (2024): 524. http://dx.doi.org/10.3390/diagnostics14050524.

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Amongst the other benefits conferred by the shift from traditional to digital pathology is the potential to use machine learning for diagnosis, prognosis, and personalization. A major challenge in the realization of this potential emerges from the extremely large size of digitized images, which are often in excess of 100,000 × 100,000 pixels. In this paper, we tackle this challenge head-on by diverging from the existing approaches in the literature—which rely on the splitting of the original images into small patches—and introducing magnifying networks (MagNets). By using an attention mechanis

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He, Jing, Ping Wang, Jingwen Cai, Dan Tang, Shaowen Yao, and Renyang Liu. "Pseudo-label attention-based multiple instance learning for whole slide image classification." Engineering Applications of Artificial Intelligence 142 (February 2025): 109908. https://doi.org/10.1016/j.engappai.2024.109908.

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Wang, Shujun, Yaxi Zhu, Lequan Yu, et al. "RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification." Medical Image Analysis 58 (December 2019): 101549. http://dx.doi.org/10.1016/j.media.2019.101549.

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Yu, Jiahui, Tianyu Ma, Yu Fu, et al. "Local-to-global spatial learning for whole-slide image representation and classification." Computerized Medical Imaging and Graphics 107 (July 2023): 102230. http://dx.doi.org/10.1016/j.compmedimag.2023.102230.

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Ma, Yingfan, Xiaoyuan Luo, Kexue Fu, and Manning Wang. "Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14263–71. http://dx.doi.org/10.1609/aaai.v38i13.29338.

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Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between end-to-end learning and proper modeling of contextual information. Most state-of-the-art methods utilize a two-stage strategy, in which they use a pre-trained model to extract features of small patches cut from a WSI and then input these features into a classification model. These methods can not pe

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Yang, Rui, Pei Liu, and Luping Ji. "ProDiv: Prototype-driven consistent pseudo-bag division for whole-slide image classification." Computer Methods and Programs in Biomedicine 249 (June 2024): 108161. http://dx.doi.org/10.1016/j.cmpb.2024.108161.

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Song, JaeYen, Soyoung Im, Sung Hak Lee, and Hyun-Jong Jang. "Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images." Diagnostics 12, no. 11 (2022): 2623. http://dx.doi.org/10.3390/diagnostics12112623.

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Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of

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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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Tourniaire, Paul, Marius Ilie, Paul Hofman, Nicholas Ayache, and Hervé Delingette. "Abstract 461: Mixed supervision to improve the classification and localization: Coherence of tumors in histological slides." Cancer Research 82, no. 12_Supplement (2022): 461. http://dx.doi.org/10.1158/1538-7445.am2022-461.

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Abstract With the growing standardization of Whole Slide Images (WSIs), deep learning algorithms have shown promising results for the automated classification and localization of tumors. Yet, it is often difficult to train such algorithms, as they usually require careful detailed annotations from expert pathologists, which are tedious to produce. This is why in general only slide-level labels are accessible while annotations of small regions (or tiles) are limited. With only slide-level information, it is difficult to obtain accurate predictions of the localization of pathological tissues insi

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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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Jayaratne, N., A. Sasikumar, S. Subasinghe, et al. "Using Deep Learning for Whole Slide Image Prostate Cancer Diagnosis and Grading in South Florida Veteran Population." American Journal of Clinical Pathology 156, Supplement_1 (2021): S141. http://dx.doi.org/10.1093/ajcp/aqab191.301.

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Abstract Introduction/Objective Prostate cancer is the most common non-cutaneous malignancy in veterans, with approximately 11,000 new prostate cancer cases diagnosed in the Veteran’s Affairs system each year. Prostate cancer diagnosis and grading can be challenging even for experienced pathologists. Although large VA medical centers have pathologists that specialize in urologic pathology, the vast majority have not. We hypothesized that the AI-augmented diagnosis and grading may provide the solution for such situations. Methods/Case Report Dataset consisted of 10,000 prostate biopsy whole sli

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Zhang, Jianxin, Chengyang Gao, Jian Sun, Xueyan Ding, and Bin Liu. "Whole slide pathological image classification of breast cancer based on mixed supervision learning." Journal of Image and Graphics 29, no. 9 (2024): 2825–36. http://dx.doi.org/10.11834/jig.230343.

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Zheng, Tingting, Kui Jiang, Hongxun Yao, Yi Xiao, and Zhongyuan Wang. "OODML: Whole Slide Image Classification Meets Online Pseudo-Supervision and Dynamic Mutual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10626–34. https://doi.org/10.1609/aaai.v39i10.33154.

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Bag-label-based multi-instance learning (MIL) has demonstrated significant performance in whole slide image (WSI) analysis, particularly in pseudo-label-based learning schemes. However, due to inaccurate feature representation and interference, existing MIL methods often yield unreliable pseudo-labels, which spawn undesired predictions. To address these issues, we propose an Online Pseudo-Supervision and Dynamic Mutual Learning (OODML) framework that enhances pseudo-label generation and feature representation while exploring their mutual learning to improve bag-level prediction. Specifically,

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Zhong, Haiqin, Meidan Ding, Cheng Zhao, Yongtao Zhang, Tianfu Wang, and Baiying Lei. "MSMMIL: Multi-scan Mamba-based Multiple Instance Learning for whole slide image classification." Knowledge-Based Systems 324 (August 2025): 113871. https://doi.org/10.1016/j.knosys.2025.113871.

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Soldatov, Sergey A., Danil M. Pashkov, Sergey A. Guda, Nikolay S. Karnaukhov, Alexander A. Guda, and Alexander V. Soldatov. "Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images." Algorithms 15, no. 11 (2022): 398. http://dx.doi.org/10.3390/a15110398.

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Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a significant amount of time and a highly qualified pathologist to decrease the high mortality rate. Recent development of artificial intelligence technologies and scanning microscopy introduced digital pathology into the field of cancer diagnosis by means of the whole-slide image (WSI). In this work, we ap

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Prasad Battula, Krishna, and B. Sai Chandana. "Multi-class Cervical Cancer Classification using Transfer Learning-based Optimized SE-ResNet152 model in Pap Smear Whole Slide Images." International journal of electrical and computer engineering systems 14, no. 6 (2023): 623. http://dx.doi.org/10.32985/ijeces.14.6.1.

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Among the main factors contributing to death globally is cervical cancer, regardless of whether it can be avoided and treated if the afflicted tissues are removed early. Cervical screening programs must be made accessible to everyone and effectively, which is a difficult task that necessitates, among other things, identifying the population's most vulnerable members. Therefore, we present an effective deep-learning method for classifying the multi-class cervical cancer disease using Pap smear images in this research. The transfer learning-based optimized SE-ResNet152 model is used for effectiv

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Schmitt, Max, Roman Christoph Maron, Achim Hekler, et al. "Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study." Journal of Medical Internet Research 23, no. 2 (2021): e23436. http://dx.doi.org/10.2196/23436.

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Background An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. Objective The objective of the study was to analyze the learnability of an exemplary selection of h

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Liu, Dehua, and Bin Hu. "A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism." Sensors 25, no. 3 (2025): 726. https://doi.org/10.3390/s25030726.

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This study introduces an innovative deep learning framework to address the limitations of traditional pathological image analysis and the pressing demand for medical resources in tumor diagnosis. With the global rise in cancer cases, manual examination by pathologists is increasingly inadequate, being both time-consuming and subject to the scarcity of professionals and individual subjectivity, thus impacting diagnostic accuracy and efficiency. Deep learning, particularly in computer vision, offers significant potential to mitigate these challenges. Automated models can rapidly and accurately p

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Srivastava, Arunima, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick, and Raghu Machiraju. "Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images." Biomedical Informatics Insights 10 (January 2018): 117822261880748. http://dx.doi.org/10.1177/1178222618807481.

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Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole sl

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Cui, Lei, Jun Feng, and Lin Yang. "Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks." Journal of Healthcare Engineering 2019 (June 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/5191630.

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Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases. However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images. In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS. Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification. Our deep network is comprised of the encoder and decoder modules. The encoder module

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Rodner, Erik, Marcel Simon, and Joachim Denzler. "Deep bilinear features for Her2 scoring in digital pathology." Current Directions in Biomedical Engineering 3, no. 2 (2017): 811–14. http://dx.doi.org/10.1515/cdbme-2017-0171.

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AbstractWe present an automated approach for rating HER2 over-expressions in given whole-slide images of breast cancer histology slides. The slides have a very high resolution and only a small part of it is relevant for the rating.Our approach is based on Convolutional Neural Networks (CNN), which are directly modelling the whole computer vision pipeline, from feature extraction to classification, with a single parameterized model. CNN models have led to a significant breakthrough in a lot of vision applications and showed promising results for medical tasks. However, the required size of trai

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Gou, Jiaxiang, Luping Ji, Pei Liu, and Mao Ye. "Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3158–66. https://doi.org/10.1609/aaai.v39i3.32325.

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Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static datasets. One of the most obvious weaknesses of these methods is that they cannot efficiently preserve and utilize previously learned knowledge. With any new data arriving, classification models are required to be re-trained on both previous and current new data. To overcome this shortcoming and break through traditional vision modality, this paper proposes

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Dolezal, James M., Sara Kochanny, Emma Dyer, et al. "Slideflow: deep learning for digital histopathology with real-time whole-slide visualization." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-024-05758-x.

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AbstractDeep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad a

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Ciga, Ozan, Tony Xu, Sharon Nofech-Mozes, Shawna Noy, Fang-I. Lu, and Anne L. Martel. "Overcoming the limitations of patch-based learning to detect cancer in whole slide images." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-88494-z.

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AbstractWhole slide images (WSIs) pose unique challenges when training deep learning models. They are very large which makes it necessary to break each image down into smaller patches for analysis, image features have to be extracted at multiple scales in order to capture both detail and context, and extreme class imbalances may exist. Significant progress has been made in the analysis of these images, thanks largely due to the availability of public annotated datasets. We postulate, however, that even if a method scores well on a challenge task, this success may not translate to good performa

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Yu, Jin-Gang, Zihao Wu, Yu Ming, et al. "Bayesian Collaborative Learning for Whole-Slide Image Classification." IEEE Transactions on Medical Imaging, 2023, 1. http://dx.doi.org/10.1109/tmi.2023.3241204.

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Ashraf, Murtaza, Willmer Rafell Quiñones Robles, Mujin Kim, Young Sin Ko, and Mun Yong Yi. "A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-05001-8.

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AbstractThis paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II error

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