# Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

arXiv: https://arxiv.org/abs/2206.05148 · HTML: https://mlsanity.com/paper/2206.05148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [Understanding data augmentation for classification: when to warp?](https://mlsanity.com/paper/1609.08764/) (2016)
- [Classification of Brain Tumours in MR Images using Deep Spatiospatial Models](https://mlsanity.com/paper/2105.14071/) (2021)
- [GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network](https://mlsanity.com/paper/1705.07999/) (2017)
- [Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images](https://mlsanity.com/paper/2006.02570/) (2020)
- [ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning](https://mlsanity.com/paper/2102.12898/) (2021)
- [ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data](https://mlsanity.com/paper/2103.09203/) (2021)
- [TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models](https://mlsanity.com/paper/2110.08429/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring](https://mlsanity.com/paper/2309.11267/) (2023)
- [Additive Class Distinction Maps using Branched-GANs](https://mlsanity.com/paper/2305.02899/) (2023)
- [Weakly Supervised Pixel-Level Annotation with Visual Interpretability](https://mlsanity.com/paper/2502.17824/) (2025)

## Abstract (arXiv)

Deep learning has demonstrated significant potential in medical imaging; however, the opacity of "black-box" models hinders clinical trust, while segmentation tasks typically necessitate labourious, 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. Code is available on GitHub: https://github.com/soumickmj/GPModels