Breast Density Quantification Using Weakly Annotated Dataset. - Mickael Tardy; Bruno Scheffer; Diana Mateus (ISBI, 2019)

Uncertainty Measurements for the Reliable Classification of Mammograms. - Mickael Tardy; Bruno Scheffer; Diana Mateus (MICCAI (6), 2019)

Lightweight U-Net for High-Resolution Breast Imaging. - Mickael Tardy; Diana Mateus (CoRR, 2020)

Trainable Summarization to Improve Breast Tomosynthesis Classification. - Mickael Tardy; Diana Mateus (MICCAI (7), 2021)

Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction. - Mickael Tardy; Diana Mateus (IEEE Trans. Medical Imaging, 2021)

Apprentissage automatique pour l'aide au diagnostic précoce du cancer du sein. (Deep learning for computer-aided early diagnosis of breast cancer). - Mickael Tardy (2021)

Multi-scale Graph Neural Networks for Mammography Classification and Abnormality Detection. - Guillaume Pelluet; Mira Rizkallah; Mickael Tardy; Oscar Acosta; Diana Mateus (MIUA, 2022)

Memory-aware curriculum federated learning for breast cancer classification. - Amelia Jiménez-Sánchez; Mickael Tardy; Miguel Ángel González Ballester; Diana Mateus; Gemma Piella (Comput. Methods Programs Biomed., 2023)

Weakly-Supervised End-to-End Framework for Pixel-Wise Description of Micro-Calcifications in Full-Resolution Mammograms. - Paul Terrassin; Mickael Tardy; Nathan Lauzeral; Nicolas Normand (ISBI, 2024)

Thick Slices for Optimal Digital Breast Tomosynthesis Classification With Deep-Learning. - Paul Terrassin; Mickael Tardy; Hassan Alhajj; Nathan Lauzeral; Nicolas Normand (Deep-Breath@MICCAI, 2024)