5 papers
LEMON: a foundation model for nuclear morphology in Computational Pathology
Loïc Chadoutaud, Alice Blondel, Hana Feki +3
Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress a…
MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models
Guillaume Balezo, Roger Trullo, Albert Pla Planas +2
Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tis…
Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Raphaël Bourgade, Guillaume Balezo, Hana Feki +6
Mitotic figures represent a key histoprognostic feature in tumor pathology, providing crucial insights into tumor aggressiveness and proliferation. However, their identification re…
Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification
Guillaume Balezo, Raphaël Bourgade, Hana Feki +8
Atypical mitotic figures (AMFs) indicate abnormal cell division associated with poor prognosis. Their detection remains difficult due to low prevalence, subtle morphology, and inte…
ConvNeXt with Histopathology-Specific Augmentations for Mitotic Figure Classification
Hana Feki, Alice Blondel, Thomas Walter
Accurate mitotic figure classification is crucial in computational pathology, as mitotic activity informs cancer grading and patient prognosis. Distinguishing atypical mitotic figu…