most citedImproving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cs.CV2020★ 1 cited

Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations

Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont +2

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlig…

eess.IV2019

Regression Constraint for an Explainable Cervical Cancer Classifier

Antoine Pirovano, Leandro G. Almeida, Said Ladjal

This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public…