brain-tumour-pfd-gste-ablation-b

Brain Tumour Hybrid Ablation B

This repository contains the trained Ablation Without PFD-B / GSTE-B 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

• Main GitHub repository: https://github.com/AnnyaB/HybridResNet50V2-RViT
• Live Hugging Face Space: https://huggingface.co/spaces/AnnyaaB/brain-tumour-pfd-gste-demo
• This model repo: https://huggingface.co/AnnyaaB/brain-tumour-pfd-gste-ablation-b

Model Summary

This ablation removes the PFD-B / GSTE-B guidance pathway for matched comparison with Hybrid B.

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:

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