Regression Constraint for an Explainable Cervical Cancer Classifier. - Antoine Pirovano; Leandro G. Almeida; Saïd Ladjal (CoRR, 2019)

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; Saïd Ladjal; Isabelle Bloch (iMIMIC/MIL3ID/LABELS@MICCAI, 2020)

Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging. - Antoine Pirovano; Hippolyte Heuberger; Sylvain Berlemont; Saïd Ladjal; Isabelle Bloch (Mach. Learn. Knowl. Extr., 2021)

Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier. - Antoine Pirovano; Leandro G. Almeida; Saïd Ladjal; Isabelle Bloch; Sylvain Berlemont (Medical Image Anal., 2021)

Computer-aided diagnosis methods for cervical cancer screening on liquid-based Pap smears using convolutional neural networks : design, optimization and interpretability. (Méthodes de diagnostic assisté par ordinateur pour le dépistage du cancer du col de l'utérus sur lames de frottis vaginal en milieu liquide basées sur les réseaux de neurones à convolutions : conception, optimisation et interprétabilité). - Antoine Pirovano (2021)