ORCID
- Vivek Singh: 0000-0003-1728-1198
- Swen Gaudl: 0000-0003-3116-3761
- Emmanuel Ifeachor: 0000-0001-8362-6292
Abstract
Deep learning shows strong promise in brain tumour classification using Magnetic Resonance Imaging (MRI), although limited interpretability constrains clinical translation. Most interpretability methods are post-hoc and yield visual attribution maps that are only weakly connected to the decision process. Clinicians prefer decisions built from evidence they can recognise and verify on MRI, rather than post-hoc explanations. Case-based models embed reasoning by comparing image evidence with learned prototypes, yielding “this looks like that” rationales at decision time and mirroring clinical reasoning. Building on this paradigm, we introduce UM–ProtoShare, which compares the input multi-sequence 3D brain MRI with a bank of shared, class-agnostic, multi-scale prototypes for pre-operative glioma grading. It returns not only a label, but a set of prototype matches that highlight where the model found support for its prediction. UM–ProtoShare uses a 3D ResNet-152 encoder, a lightweight UNet–style decoder with gated encoder–decoder fusions, and a normalised soft-masked mapping module to align and highlight prototype evidence on MRI. On BraTS-2020, ablations show additive benefits from the normalised mapping module, prototype sharing, multi-scale prototypes, and the decoder with gated fusions. Varying the allocation of prototypes across scales identifies a balanced accuracy–interpretability configuration that closely approaches a strong 3D ResNet-152 in classification performance (Balanced Accuracy: 88.40 ± 2.80; 1.48 percentage points lower) while delivering more faithful and spatially precise evidence than prior case-based models, with Activation Precision (AP) 88.72 ± 1.60 (+11.0% vs MProtoNet; +4.0% vs MAProtoNet) and Incremental Deletion Score (IDS) 5.10 ± 1.30 (lower is better, −32.3% vs MProtoNet, −25.3% vs MAProtoNet).
Publication Date
2026-07-08
Event
9th International Conference on Medical Imaging with Deep Learning, MIDL 2026
Publication Title
Proceedings of Machine Learning Research
Volume
315
Deposit Date
2026-08-24
Funding
This work was supported by a UK EPSRC studentship and, in part, by Brain Tumour Research.
Additional Links
Keywords
Brain Tumour Classification, Case-based Models, Interpretable Deep Learning, Multi-sequence 3D MRI
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
First Page
637
Last Page
669
Recommended Citation
Golbaf, A., Singh, V., Gaudl, S., & Ifeachor, E. (2026) 'UM–ProtoShare: UNet-Guided, Multi-scale Shared Prototypes for Interpretable Brain Tumour Classification Using Multi-sequence 3D MRI', Proceedings of Machine Learning Research, 315, pp. 637-669. Retrieved from https://pearl.plymouth.ac.uk/secam-research/2261
