HistoGraph brings together three computer science partners and three medical centres to develop novel techniques for the automatic diagnosis of disease from whole-slide images. The project is developing a graph-based framework that uses state-of-the-art, stain-invariant feature extraction techniques to integrate multi-modal information at the slide level and automatically predict disease severity grading.
News:
- 2025
- Sep – N. Kormann joined l'X as a PhD student under HistoGraph.
- Jul – We presented some of the project's findings to Roche.
- Jul – Z. Nisar joined ICube as a postdoctoral researcher under HistoGraph.
- Jun – Our article “HIEGNet: A Heterogeneous Graph Neural Network Including the Immune Environment in Glomeruli Classification”, authored by N. Kormann et al., was accepted at Medical Imaging with Deep Learning (MIDL) 2025.
- 2024
- Jun – The project's first article (co-funded by the ITI HealthTech at the University of Strasbourg), titled “StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation” and authored by A. Alhaj Abdo et al., was accepted at the COMPAYL Workshop at MICCAI 2024.
- Apr – Project kickoff!