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Semi-supervised learning in cancer diagnostics
In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making. Most of these classifiers are based on supervised learning (SL) that needs...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329803/ https://www.ncbi.nlm.nih.gov/pubmed/35912249 http://dx.doi.org/10.3389/fonc.2022.960984 |
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author | Eckardt, Jan-Niklas Bornhäuser, Martin Wendt, Karsten Middeke, Jan Moritz |
author_facet | Eckardt, Jan-Niklas Bornhäuser, Martin Wendt, Karsten Middeke, Jan Moritz |
author_sort | Eckardt, Jan-Niklas |
collection | PubMed |
description | In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making. Most of these classifiers are based on supervised learning (SL) that needs time- and cost-intensive manual labeling of samples by medical experts for model training. Semi-supervised learning (SSL), however, works with only a fraction of labeled data by including unlabeled samples for information abstraction and thus can utilize the vast discrepancy between available labeled data and overall available data in cancer diagnostics. In this review, we provide a comprehensive overview of essential functionalities and assumptions of SSL and survey key studies with regard to cancer care differentiating between image-based and non-image-based applications. We highlight current state-of-the-art models in histopathology, radiology and radiotherapy, as well as genomics. Further, we discuss potential pitfalls in SSL study design such as discrepancies in data distributions and comparison to baseline SL models, and point out future directions for SSL in oncology. We believe well-designed SSL models to strongly contribute to computer-guided diagnostics in malignant disease by overcoming current hinderances in the form of sparse labeled and abundant unlabeled data. |
format | Online Article Text |
id | pubmed-9329803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93298032022-07-29 Semi-supervised learning in cancer diagnostics Eckardt, Jan-Niklas Bornhäuser, Martin Wendt, Karsten Middeke, Jan Moritz Front Oncol Oncology In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making. Most of these classifiers are based on supervised learning (SL) that needs time- and cost-intensive manual labeling of samples by medical experts for model training. Semi-supervised learning (SSL), however, works with only a fraction of labeled data by including unlabeled samples for information abstraction and thus can utilize the vast discrepancy between available labeled data and overall available data in cancer diagnostics. In this review, we provide a comprehensive overview of essential functionalities and assumptions of SSL and survey key studies with regard to cancer care differentiating between image-based and non-image-based applications. We highlight current state-of-the-art models in histopathology, radiology and radiotherapy, as well as genomics. Further, we discuss potential pitfalls in SSL study design such as discrepancies in data distributions and comparison to baseline SL models, and point out future directions for SSL in oncology. We believe well-designed SSL models to strongly contribute to computer-guided diagnostics in malignant disease by overcoming current hinderances in the form of sparse labeled and abundant unlabeled data. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9329803/ /pubmed/35912249 http://dx.doi.org/10.3389/fonc.2022.960984 Text en Copyright © 2022 Eckardt, Bornhäuser, Wendt and Middeke https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Eckardt, Jan-Niklas Bornhäuser, Martin Wendt, Karsten Middeke, Jan Moritz Semi-supervised learning in cancer diagnostics |
title | Semi-supervised learning in cancer diagnostics |
title_full | Semi-supervised learning in cancer diagnostics |
title_fullStr | Semi-supervised learning in cancer diagnostics |
title_full_unstemmed | Semi-supervised learning in cancer diagnostics |
title_short | Semi-supervised learning in cancer diagnostics |
title_sort | semi-supervised learning in cancer diagnostics |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329803/ https://www.ncbi.nlm.nih.gov/pubmed/35912249 http://dx.doi.org/10.3389/fonc.2022.960984 |
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