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High-Dimensional Statistical Learning and Its Application to Oncological Diagnosis by Radiomics

Charles Bouveyron 1, 2, 3
2 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems, UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné
Abstract : Statistical learning is today playing an increasing role in many scientific fields as varied as medicine, imagery, biology, and astronomy. Scientific advances in recent years have significantly increased measurement and calculation capabilities, and it is now difficult for a human operator to process such data exhaustively in a timely manner. In particular, many medical specialties such as medical imaging, radiology, and genomics have benefited in recent decades from major technological developments. In some instances these developments have led specialists in these fields to rethink their data practice.
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https://hal.archives-ouvertes.fr/hal-02516907
Contributor : Charles Bouveyron <>
Submitted on : Tuesday, March 24, 2020 - 10:46:38 AM
Last modification on : Monday, October 12, 2020 - 2:28:06 PM

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Charles Bouveyron. High-Dimensional Statistical Learning and Its Application to Oncological Diagnosis by Radiomics. Healthcare and Artificial Intelligence, Springer International Publishing, pp.121-128, 2020, ⟨10.1007/978-3-030-32161-1_17⟩. ⟨hal-02516907⟩

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