{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:11:21Z","timestamp":1771305081900,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Operational Program \u201cIntegrated Infrastructure\u201d","award":["313011V446"],"award-info":[{"award-number":["313011V446"]}]},{"name":"European Regional Development Fund","award":["313011V446"],"award-info":[{"award-number":["313011V446"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research. The popularity of CNNs has led to a rapid growth in the number of works related to CNNs in histopathology. This paper aims to provide a clear overview for better navigation. In this paper, recent DL-based classification studies in histopathology using strongly annotated data have been reviewed. All the works have been categorized from two points of view. First, the studies have been categorized into three groups according to the training approach and model construction: 1. fine-tuning of pre-trained networks for one-stage classification, 2. training networks from scratch for one-stage classification, and 3. multi-stage classification. Second, the papers summarized in this study cover a wide range of applications (e.g., breast, lung, colon, brain, kidney). To help navigate through the studies, the classification of reviewed works into tissue classification, tissue grading, and biomarker identification was used.<\/jats:p>","DOI":"10.3390\/computation11040081","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T05:29:29Z","timestamp":1681450169000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8309-1849","authenticated-orcid":false,"given":"Dominika","family":"Petr\u00edkov\u00e1","sequence":"first","affiliation":[{"name":"Cell-in-Fluid Biomedical Modelling & Computations Group, Faculty of Management Science and Informatics, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"},{"name":"Research Centre, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0389-7891","authenticated-orcid":false,"given":"Ivan","family":"Cimr\u00e1k","sequence":"additional","affiliation":[{"name":"Cell-in-Fluid Biomedical Modelling & Computations Group, Faculty of Management Science and Informatics, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"},{"name":"Research Centre, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4103\/2153-3539.68332","article-title":"Digital images and the future of digital pathology: From the 1st Digital Pathology Summit, New Frontiers in Digital Pathology, University of Nebraska Medical Center, Omaha, Nebraska 14\u201315 May 2010","volume":"1","author":"Pantanowitz","year":"2010","journal-title":"J. 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