L. Le Henaff

Explainability and Clinical Trust of Deep Learning in Glioblastoma Treatment Efficacy Prediction: A Comprehensive Framework.

Glioblastoma (GBM) treatment response is highly heterogeneous. Predicting outcomes from pre-treatment MRI is a critical unmet need, yet the clinical adoption of "black box" deep learning (DL) models is hindered by a lack of transparency. Robust and holistic validation frameworks still need to be developed to allow clin...

Biomedical Imaging CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment response prediction...