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This repository contains the trained Hybrid B — PFD-B + GSTE-B checkpoint from the project:
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This repository contains the trained Hybrid B — 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
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1 fonteVerificado 16 de set.
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1 artefatoTrechos de fonte
2 trechosHybrid B uses PFD-B and GSTE-B, where pathology-focused guidance affects both the CNN descriptor and transformer guidance pathway with 196 patch tokens.
The checkpoint is a PyTorch hybrid CNN–Transformer model for four-class brain MRI classification:
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.Recorded held-out test-set performance from the project repository:
| Model | Test Accuracy | Macro F1 |
|---|---|---|
| Hybrid B — PFD-B + GSTE-B | 0.9852 | 0.9849 |
This model is released for research reproducibility, educational inspection, and comparison with the associated guided and ablation variants.
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.
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
--- license: mit library_name: pytorch pipeline_tag: image-classification tags: - medical-imaging - brain-mri - brain-tumour-classification - pytorch - cnn-transformer - explainable-ai - gradcam - attention-rollout - shortcut-learning - pfd-gste metrics: - accuracy - f1 --- # Brain Tumour PFD-GSTE Hybrid B This repository contains the trained **Hybrid B — 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-hybrid-b ## Model Summary Hybrid B uses PFD-B and GSTE-B, where pathology-focused guidance affects both the CNN descriptor and transformer guidance pathway with 196 patch 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 B — PFD-B + GSTE-B | 0.9852 | 0.9849 | ## 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 benc...
Source context: 0 downloads · 1 likes · Pipeline image-classification · Library pytorch · Repo AnnyaaB/brain-tumour-pfd-gste-hybrid-b