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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...

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Main Authors: Eckardt, Jan-Niklas (Author) , Bornhäuser, Martin (Author) , Wendt, Karsten (Author) , Middeke, Jan Moritz (Author)
Format: Article (Journal)
Language:English
Published: 14 July 2022
In: Frontiers in oncology
Year: 2022, Volume: 12, Pages: 1-10
ISSN:2234-943X
DOI:10.3389/fonc.2022.960984
Online Access:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.3389/fonc.2022.960984
Verlag, lizenzpflichtig, Volltext: https://www.frontiersin.org/articles/10.3389/fonc.2022.960984
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Author Notes:Jan-Niklas Eckardt, Martin Bornhäuser, Karsten Wendt and Jan Moritz Middeke

MARC

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520 |a 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. 
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