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Communication Dans Un Congrès Année : 2022

Separable vs. End-to-End Learning: A Critical Examination of Learning Paradigms

Résumé

Machine learning is undoubtedly becoming more and more integrated into medical image computing research and practice. As with any conceptual or technological tool, this exposure requires the community to better understand the logical and philosophical foundations underpinning its use. One of the these (largely unexamined) areas is the learning paradigm that motivates everything from problem decomposition to architecture selection to clinical regulation. This article examines one of the spectra that defines the learning paradigm, specifically the spectrum between end-to-end and separable learning, in order to analyse what values drive solutions towards or away from either extreme. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Dates et versions

hal-03931155 , version 1 (09-01-2023)

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John S H Baxter. Separable vs. End-to-End Learning: A Critical Examination of Learning Paradigms. 1st International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2022, the 12th International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2022, and the 2nd International Workshop on Topological Data Analysis for Biomedical Imaging, TDA4BiomedicalImaging 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Sep 2022, Singapore, Singapore. pp.26-37, ⟨10.1007/978-3-031-23223-7_3⟩. ⟨hal-03931155⟩
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