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Journal articles on the topic 'Whole slide images classification'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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El-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed M. Soliman, and Sherif A. Sami. "Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN." IEEE Access 9 (2021): 88429–38. http://dx.doi.org/10.1109/access.2021.3076158.

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Yoshida, Hiroshi, Yoshiko Yamashita, Taichi Shimazu, et al. "Automated histological classification of whole slide images of colorectal biopsy specimens." Oncotarget 8, no. 53 (2017): 90719–29. http://dx.doi.org/10.18632/oncotarget.21819.

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Xu, Hongming, Sunho Park, and Tae Hyun Hwang. "Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (2020): 1871–82. http://dx.doi.org/10.1109/tcbb.2019.2941195.

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Hassanpour, Saeed, Bruno Korbar, AndreaM Olofson, et al. "Deep learning for classification of colorectal polyps on whole-slide images." Journal of Pathology Informatics 8, no. 1 (2017): 30. http://dx.doi.org/10.4103/jpi.jpi_34_17.

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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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Yoshida, Hiroshi, Taichi Shimazu, Tomoharu Kiyuna, et al. "Automated histological classification of whole-slide images of gastric biopsy specimens." Gastric Cancer 21, no. 2 (2017): 249–57. http://dx.doi.org/10.1007/s10120-017-0731-8.

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17

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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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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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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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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Zhao, Boxuan, Jun Zhang, Deheng Ye, et al. "RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3570–78. http://dx.doi.org/10.1609/aaai.v37i3.25467.

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Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method fo

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Kallipolitis, Athanasios, Kyriakos Revelos, and Ilias Maglogiannis. "Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images." Algorithms 14, no. 10 (2021): 278. http://dx.doi.org/10.3390/a14100278.

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The extended utilization of digitized Whole Slide Images is transforming the workflow of traditional clinical histopathology to the digital era. The ongoing transformation has demonstrated major potentials towards the exploitation of Machine Learning and Deep Learning techniques as assistive tools for specialized medical personnel. While the performance of the implemented algorithms is continually boosted by the mass production of generated Whole Slide Images and the development of state-of the-art deep convolutional architectures, ensemble models provide an additional methodology towards the

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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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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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Gupta, Pushpanjali, Yenlin Huang, Prasan Kumar Sahoo, et al. "Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning." Diagnostics 11, no. 8 (2021): 1398. http://dx.doi.org/10.3390/diagnostics11081398.

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Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of develo

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Xu, Hongming, Cheng Lu, Richard Berendt, Naresh Jha, and Mrinal Mandal. "Automated analysis and classification of melanocytic tumor on skin whole slide images." Computerized Medical Imaging and Graphics 66 (June 2018): 124–34. http://dx.doi.org/10.1016/j.compmedimag.2018.01.008.

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Tsuneki, Masayuki, and Fahdi Kanavati. "Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images." PLOS ONE 17, no. 11 (2022): e0275378. http://dx.doi.org/10.1371/journal.pone.0275378.

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The primary screening by automated computational pathology algorithms of the presence or absence of adenocarcinoma in biopsy specimens (e.g., endoscopic biopsy, transbronchial lung biopsy, and needle biopsy) of possible primary organs (e.g., stomach, colon, lung, and breast) and radical lymph node dissection specimen is very useful and should be a powerful tool to assist surgical pathologists in routine histopathological diagnostic workflow. In this paper, we trained multi-organ deep learning models to classify adenocarcinoma in biopsy and radical lymph node dissection specimens whole slide im

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Sun, Zh, A. Khvostikov, A. Krylov, A. Sethi, I. Mikhailov, and P. Malkov. "Joint Super-resolution and Tissue Patch Classification for Whole Slide Histological Images." Programming and Computer Software 50, no. 3 (2024): 257–63. http://dx.doi.org/10.1134/s0361768824700063.

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Innani, Shubham, W. Robert Bell, MacLean Nasrallah, Bhakti Baheti, and Spyridon Bakas. "Abstract 6247: Artificial intelligence predicts 2021 WHO glioma subtypes from whole slide images." Cancer Research 85, no. 8_Supplement_1 (2025): 6247. https://doi.org/10.1158/1538-7445.am2025-6247.

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Abstract Diagnosis of diffuse glioma according to the WHO 2021 classification criteria mandate the integration of histologic features with molecular profiling. However, molecular profiling is expensive, time-demanding, and when not available leads to the ‘not-otherwise-specified' status. We seek interpretable AI-based classification of glioma, as oligodendroglioma, astrocytoma, or glioblastoma, from H&E-stained slides alone. We identified 2, 114 multi-institutional whole slide images (WSIs), from two independent retrospective glioma collections, following reclassification according to the

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Mahmood, F., C. J. Robbins, S. Perincheri, and R. Torres. "Applying Deep Learning Cancer Subtyping Algorithms Trained on Physical Slides to Multiphoton Imaging of Unembedded Samples." American Journal of Clinical Pathology 158, Supplement_1 (2022): S117. http://dx.doi.org/10.1093/ajcp/aqac126.248.

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Abstract Introduction/Objective Deep learning algorithms on digital images of physical tissue slides have shown potential improvements in accuracy and precision of diagnostic interpretation of neoplastic histology. Clustering-constrained- attention multiple-instance learning (CLAM) is one such method that identifies diagnostic sub-regions to accurately classify whole slides. Often, algorithm performance degrades when deployed on datasets that differ from the original set and it is subject to physical slide preparation variability. New multiphoton imaging modalities have potential workflow and

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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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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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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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Tavolara, Thomas E., Metin N. Gurcan, and M. Khalid Khan Niazi. "Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels." Cancers 14, no. 23 (2022): 5778. http://dx.doi.org/10.3390/cancers14235778.

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Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of i

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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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Wang, Ching-Wei, Sheng-Chuan Huang, Yu-Ching Lee, Yu-Jie Shen, Shwu-Ing Meng, and Jeff L. Gaol. "Deep learning for bone marrow cell detection and classification on whole-slide images." Medical Image Analysis 75 (January 2022): 102270. http://dx.doi.org/10.1016/j.media.2021.102270.

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Morkūnas, Mindaugas, Povilas Treigys, Jolita Bernatavičienė, Arvydas Laurinavičius, and Gražina Korvel. "Machine Learning Based Classification of Colorectal Cancer Tumour Tissue in Whole-Slide Images." Informatica 29, no. 1 (2018): 75–90. http://dx.doi.org/10.15388/informatica.2018.158.

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Cho, Kyung-Ok, Sung Hak Lee, and Hyun-Jong Jang. "Feasibility of fully automated classification of whole slide images based on deep learning." Korean Journal of Physiology & Pharmacology 24, no. 1 (2020): 89. http://dx.doi.org/10.4196/kjpp.2020.24.1.89.

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Raza, Manahil, Ruqayya Awan, Raja Muhammad Saad Bashir, Talha Qaiser, and Nasir M. Rajpoot. "Dual attention model with reinforcement learning for classification of histology whole-slide images." Computerized Medical Imaging and Graphics 118 (December 2024): 102466. http://dx.doi.org/10.1016/j.compmedimag.2024.102466.

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Sertel, O., J. Kong, H. Shimada, U. V. Catalyurek, J. H. Saltz, and M. N. Gurcan. "Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development." Pattern Recognition 42, no. 6 (2009): 1093–103. http://dx.doi.org/10.1016/j.patcog.2008.08.027.

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Yingli, Zhao, Ding Weilong, You Qinghua, et al. "Classification of whole slide images of breast histopathology based on spatial correlation characteristics." Journal of Image and Graphics 28, no. 4 (2023): 1134–45. http://dx.doi.org/10.11834/jig.211133.

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Shakarami, Ashkan, Lorenzo Nicolè, Matteo Terreran, Angelo Paolo Dei Tos, and Stefano Ghidoni. "TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images." Biomedical Signal Processing and Control 84 (July 2023): 104812. http://dx.doi.org/10.1016/j.bspc.2023.104812.

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Fu, Yan, Fanlin Zhou, Xu Shi, et al. "Classification of adenoid cystic carcinoma in whole slide images by using deep learning." Biomedical Signal Processing and Control 84 (July 2023): 104789. http://dx.doi.org/10.1016/j.bspc.2023.104789.

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Pirovano, Antoine, Hippolyte Heuberger, Sylvain Berlemont, SaÏd Ladjal, and Isabelle Bloch. "Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging." Machine Learning and Knowledge Extraction 3, no. 1 (2021): 243–62. http://dx.doi.org/10.3390/make3010012.

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Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification with the formalization of the design of WSI classification architectures and propose a piece-wise interp

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Sun, Shenghuan, Jacob Cleave, Linlin Wang, et al. "Deep Learning for Morphology-Based, Bone Marrow Cell Classification." Blood 142, Supplement 1 (2023): 2841. http://dx.doi.org/10.1182/blood-2023-172654.

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The morphological classification of cells in bone marrow aspirate (BMA) is central to the diagnosis of hematologic diseases, including leukemias. Despite being a critical task, its monotonous, time-consuming nature and dependency on highly skilled clinical experts makes it prone to human error. Such errors can lead to delays and misdiagnoses that negatively impact patient care. To counter these challenges, we curated an expansive dataset of more than 40,000 hematopathologist consensus-annotated single-cell images, extracted from BMA whole slide images (WSIs), each annotated into one of 23 dist

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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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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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Huang, Jin, Liye Mei, Mengping Long, et al. "BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images." Bioengineering 9, no. 6 (2022): 261. http://dx.doi.org/10.3390/bioengineering9060261.

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Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI da

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Ahmad Fauzi, Mohammad Faizal, Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Fareed Jamaluddin, et al. "Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma." Diagnostics 12, no. 12 (2022): 3093. http://dx.doi.org/10.3390/diagnostics12123093.

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Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific

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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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