Improve the Interpretability of Attention: A Fast, Accurate, and Interpretable High-Resolution Attention Model. - Tristan Gomez; Suiyi Ling; Thomas Fréour; Harold Mouchère (CoRR, 2021)

Comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study. - Tristan Gomez; Thomas Fréour; Harold Mouchère (ICPR Workshops (3), 2022)

Metrics for Saliency Map Evaluation of Deep Learning Explanation Methods. - Tristan Gomez; Thomas Fréour; Harold Mouchère (ICPRAI (1), 2022)

Super-Focus: Domain Adaptation for Embryo Imaging via Self-supervised Focal Plane Regression. - Chloe He 002; Céline Jacques; Jérôme Chambost; Jonas Malmsten; Koen Wouters; Thomas Fréour; Nikica Zaninovic; Cristina Hickman; Francisco Vasconcelos 001 (MICCAI (2), 2022)

Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction. - Tristan Gomez; Magalie Feyeux; Nicolas Normand; Laurent David; Perrine Paul-Gilloteaux; Thomas Fréour; Harold Mouchère (CoRR, 2022)

BR-NPA: A non-parametric high-resolution attention model to improve the interpretability of attention. - Tristan Gomez; Suiyi Ling; Thomas Fréour; Harold Mouchère (Pattern Recognit., 2022)

Embryo Graphs: Predicting Human Embryo Viability from 3D Morphology. - Chloe He 002; Neringa Karpaviciute; Rishabh Hariharan; Céline Jacques; Jérôme Chambost; Jonas Malmsten; Nikica Zaninovic; Koen Wouters; Thomas Fréour; Cristina Hickman; Francisco Vasconcelos 001 (MICCAI (4), 2024)