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)

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)

Attention spatiale artificielle pour des modèles profonds interprétables de qualité embryonnaire. (Artificial spatial attention for interpretable deep models of embryonic quality). - Tristan Gomez (2022)

Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification. - Tristan Gomez; Harold Mouchère (CoRR, 2023)

Computing and evaluating saliency maps for image classification: a tutorial. - Tristan Gomez; Harold Mouchère (J. Electronic Imaging, 2023)