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| 037 | _ | _ | |a DZNE-2026-00388 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Chatterjee, Soumick |0 0000-0001-7594-1188 |b 0 |
| 245 | _ | _ | |a Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification |
| 260 | _ | _ | |a Amsterdam |c 2026 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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% CI: 0.715–0.739) was recorded. Notably, on a subset of tumour-only images, the best model achieved an accuracy of 98.7%, outperforming state-of-the-art glioma grading binary classifiers. Furthermore, comparative Precision-Recall analysis validated the framework’s robustness against severe class imbalance, establishing a direct correlation between diagnostic confidence and segmentation fidelity. These results demonstrate that the proposed framework successfully combines high diagnostic accuracy with essential transparency, offering a promising direction for trustworthy clinical decision support. |
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| 700 | 1 | _ | |a Yassin, Hadya |0 0000-0003-0987-505X |b 1 |
| 700 | 1 | _ | |a Dubost, Florian |b 2 |
| 700 | 1 | _ | |a Nürnberger, Andreas |0 0000-0003-4311-0624 |b 3 |
| 700 | 1 | _ | |a Speck, Oliver |0 P:(DE-2719)2810706 |b 4 |e Last author |
| 773 | _ | _ | |a 10.1016/j.neucom.2026.133460 |g Vol. 682, p. 133460 - |0 PERI:(DE-600)1479006-3 |p 133460 |t Neurocomputing |v 682 |y 2026 |x 0925-2312 |
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