Constrained non-negative networks for a more explainable and interpretable classification - Physics, Radiobiology, Medical Imaging, and Simulation (PRIMES)
Conference Papers Year : 2024

Constrained non-negative networks for a more explainable and interpretable classification

Domains

Machine Learning [stat.ML] Medical Imaging Signal and Image Processing
Fichier principal
Vignette du fichier
wargnier-MIDL2024-monotone.pdf (1003.56 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Michaël Sdika  : Connect in order to contact the contributor

https://hal.science/hal-04561860

Submitted on : Sunday, April 28, 2024-9:20:57 AM

Last modification on : Wednesday, July 3, 2024-9:00:06 AM

Download

Dates and versions

hal-04561860 , version 1 (28-04-2024)

Identifiers

  • HAL Id : hal-04561860 , version 1

Cite

Valentine Wargnier-Dauchelle, Thomas Grenier, Françoise Durand-Dubief, François Cotton, Michaël Sdika. Constrained non-negative networks for a more explainable and interpretable classification. Medical Imaging with Deep Learning, Jul 2024, Paris, France. ⟨hal-04561860⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

UNIV-ST-ETIENNE CNRS UNIV-LYON1 INSA-LYON CREATIS PRIMES PRIMES_WP4 GENCI INSA-GROUPE UDL ANR
50 View
71 Download

Share

More