TY  - JOUR
AU  - Chatterjee, Soumick
AU  - Yassin, Hadya
AU  - Dubost, Florian
AU  - Nürnberger, Andreas
AU  - Speck, Oliver
TI  - Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
JO  - Neurocomputing
VL  - 682
SN  - 0925-2312
CY  - Amsterdam
PB  - Elsevier
M1  - DZNE-2026-00388
SP  - 133460
PY  - 2026
AB  - Deep learning has demonstrated significant potential in medical imaging; however, the opacity of “black-box” models hinders clinical trust, while segmentation tasks typically necessitate laborious, hard-to-obtain pixel-wise annotations. To address these challenges simultaneously, this paper introduces a framework for three inherently explainable classifiers (GP-UNet, GP-ShuffleUNet, and GP-ReconResNet). By integrating a global pooling mechanism, these networks generate localisation heatmaps that directly influence classification decisions, offering inherent interpretability without relying on potentially unreliable post-hoc methods. These heatmaps are subsequently thresholded to achieve weakly-supervised segmentation, requiring only image-level classification labels for training. Validated on two datasets for multi-class brain tumour classification, the proposed models achieved a peak F1-score of 0.93. For the weakly-supervised segmentation task, a median Dice score of 0.728 (95
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.neucom.2026.133460
UR  - https://pub.dzne.de/record/286092
ER  -