Brain Tumour PFD-GSTE Hybrid A
This repository contains the trained Hybrid A — PFD-A + GSTE-A checkpoint from the project:
Mitigating Shortcut Learning in Brain Tumour MRI Classification
BSc Artificial Intelligence Project, University of Hertfordshire
Author: Riya Basak
Supervisor: Dr Kheng Lee Koay
Links
Model Summary
Hybrid A uses PFD-A and GSTE-A, where pathology-focused guidance gates the transformer-token pathway and reweights 49 CNN feature tokens.
The checkpoint is a PyTorch hybrid CNN–Transformer model for four-class brain MRI classification:
- glioma
- meningioma
- pituitary
- notumor
Files
best_model.pt — trained PyTorch checkpoint.architecture.py — model architecture source file.xai.py — model-specific XAI helper file.models/hybrid_model.py — loading wrapper used by the app workflow.pfd_gste/ — local PFD-GSTE guidance modules used for release reproducibility.model_config.json — model metadata and preprocessing configuration.SHA256SUMS — checkpoint checksum for verification.
Evaluation Summary
Recorded held-out test-set performance from the project repository:
| Model | Test Accuracy | Macro F1 |
|---|
| Hybrid A — PFD-A + GSTE-A | 0.9875 | 0.9875 |
Intended Use
This model is released for research reproducibility, educational inspection, and comparison with the associated guided and ablation variants.
Limitations
- The model was evaluated on a single benchmark curation.
- The checkpoint is not externally clinically validated.
- Grad-CAM++ and attention rollout are qualitative inspection tools, not clinical annotations.
- Outputs may be incorrect and should not be used for medical decision-making.
- The dataset is not redistributed in this model repository; users should follow the GitHub reproduction instructions to obtain the benchmark dataset separately.
Medical Disclaimer
This model is for research and educational use only. It is not a certified medical device and must not be used for clinical diagnosis, patient management, treatment decisions, or emergency medical use.
Citation
If this checkpoint, code, or PFD-GSTE guidance modules are useful in your work, please cite:
Basak, R. (2026).
Mitigating Shortcut Learning in Brain Tumour MRI Classification. BSc Artificial Intelligence Project, University of Hertfordshire. Available at:
https://github.com/AnnyaB/HybridResNet50V2-RViT
