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Apprentissage statistique en grande dimension et application au diagnostic oncologique par radiomique

Abstract : With the increase in measurement capabilities, many medical disciplines have seen their practices deeply modified because of the dimensionality of the data. Although these technical improvements promise significant advances in medical research, the statistical learning methods must be able to cope with the problems encountered in those high-dimensional spaces. The subspace classification and "sparse" methods introduced in recent years propose to meet this expectation. This article presents a quick overview of these difficulties and the proposed solutions, as well as an illustration of the use of one of these solutions for oncology diagnosis with radiomics.
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https://hal.archives-ouvertes.fr/hal-01884468
Contributor : Charles Bouveyron Connect in order to contact the contributor
Submitted on : Monday, October 1, 2018 - 9:36:12 AM
Last modification on : Thursday, August 4, 2022 - 4:58:08 PM
Long-term archiving on: : Wednesday, January 2, 2019 - 12:43:27 PM

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Charles Bouveyron. Apprentissage statistique en grande dimension et application au diagnostic oncologique par radiomique. Cédric Villani; Bernard Nordlinger. Santé et intelligence artificielle, CNRS Editions, pp.179-189, 2018. ⟨hal-01884468⟩

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