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A systematic comparison of deep learning methods for Gleason grading and scoring
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Durán López, Lourdes | es |
dc.creator | Marini, Niccolo | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.creator | Atzori, Manfredo | es |
dc.creator | Muller, Henning | es |
dc.date.accessioned | 2024-06-27T09:56:15Z | |
dc.date.available | 2024-06-27T09:56:15Z | |
dc.date.issued | 2024-07 | |
dc.identifier.issn | 1361-8415 | es |
dc.identifier.issn | 1361-8423 | es |
dc.identifier.uri | https://hdl.handle.net/11441/160921 | |
dc.description.abstract | Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available. | es |
dc.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Computational pathology | es |
dc.subject | Deep learning | es |
dc.subject | Prostate cancer | es |
dc.subject | Multiple-instance learning | es |
dc.subject | Weak supervision | es |
dc.subject | Full supervision | es |
dc.subject | Semi-supervision | es |
dc.title | A systematic comparison of deep learning methods for Gleason grading and scoring | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | PROMETEO AT17_5410_USE | es |
dc.relation.projectID | DAFNE US-1381619 | es |
dc.relation.projectID | PID2019-105556GB-C33 | es |
dc.relation.projectID | EU H2020 825292 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1361841524001166?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.media.2024.103191 | es |
dc.contributor.group | Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores | es |
dc.journaltitle | Medical Image Analysis | es |
dc.publication.volumen | 95 | es |
dc.publication.issue | 103191 | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | European Union (UE). H2020 | es |
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