Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer's Disease Using Weakly Annotated Whole Slide Histopathological Images. - Gabriel Jimenez 001; Anuradha Kar; Mehdi Ounissi; Léa Ingrassia; Susana Boluda; Benoît Delatour; Lev Stimmer; Daniel Racoceanu (MICCAI (2), 2022)

Tau protein discrete aggregates in Alzheimer's disease: neuritic plaques and tangles detection and segmentation using computational histopathology. - Kristyna Manousková; Valentin Abadie; Mehdi Ounissi; Gabriel Jimenez 001; Lev Stimmer; Benoît Delatour; Stanley Durrleman; Daniel Racoceanu (Digital and Computational Pathology, 2022)

Efficient 3D reconstruction of whole slide images in melanoma. - J. Arslan; Mehdi Ounissi; H. Luo; M. Lacroix; P. Dupré; P. Kumar; Arran Hodgkinson; S. Dandou; Romain M. Larive; C. Pignodel; L. Le Cam; Ovidiu Radulescu; et al. (Digital and Computational Pathology, 2023)

Phagocytosis Unveiled: A Scalable and Interpretable Deep learning Framework for Neurodegenerative Disease Analysis. - Mehdi Ounissi; Morwena Latouche; Daniel Racoceanu (CoRR, 2023)

Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images. - Mehdi Ounissi; Ilias Sarbout; Jean-Pierre Hugot; Christine Martinez-Vinson; Dominique Berrebi; Daniel Racoceanu (CoRR, 2024)

Decoding the Black Box: Enhancing Interpretability and Trust in Artificial Intelligence for Biomedical Imaging - a Step Toward Responsible Artificial Intelligence. (Décoder la boîte noire: améliorer l'interprétabilité et la confiance en l'Intelligence Artificielle pour l'imagerie biomédicale - un pas vers une Intelligence Artificielle responsable). - Mehdi Ounissi (2024)