Lizzi, F. (2023) Deep learning applied to medical image analysis: Epistemology and data. Il nuovo cimento C, 46 (4). pp. 1-4. ISSN 1826-9885
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Abstract
Deep learning (DL) is changing the way we analyze medical im ages. What do these changes consist of? According to some interpretations, AI epistemology is moving towards a new paradigm called the “fourth paradigm”, in which theory, hypothesis and experiment are going to be unified through data. In this context, the famous statement “Correlation is enough” seems to be an effective way to describe this evolution. This change raises several issues related to medical image analysis. One of the most interesting is about data. Data are a main part of DL development especially because DL algorithms are not explainable. Medical images data sets are scarce, underpopulated, and usually do not contain acquisition and reconstruction parameters. It is only by knowing data characteristics that we can define the boundaries in which an algorithm can properly work.
| Item Type: | Article |
|---|---|
| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 08 Mar 2024 13:49 |
| Last Modified: | 08 Mar 2024 13:49 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/22715 |
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