Explainable Bone-Lesion Screening for £5
💡See how DenseNet-121 medical screening was deployed cheaply—with safeguards worth scrutinizing.
⚡ 30-Second TL;DR
What Changed
DenseNet-121 was trained on 3,746 BTXRD radiographs across normal, benign, and malignant classes.
Why It Matters
This project demonstrates that low-cost cloud infrastructure can host a medically oriented ML prototype with basic safeguards and interpretability. It is not evidence of clinical readiness: the small dataset, class imbalance, heuristic OOD detection, and single-reviewer labeling process require substantial validation before real-world medical use.
What To Do Next
Replicate the prototype’s validation setup with patient-level splits, AUROC and sensitivity-at-specificity metrics, then stress-test the hand-tuned OOD gate before considering deployment.
Key Points
- •DenseNet-121 was trained on 3,746 BTXRD radiographs across normal, benign, and malignant classes.
- •Focal loss, inverse-frequency weighting, validation-only threshold selection, and post-hoc calibration address the 9.1% malignant-class imbalance.
- •An explainability layer uses heatmaps, while a hand-tuned out-of-distribution gate rejects unrelated inputs such as hotdog photos.
- •A single reviewer assigns ground truth to user-flagged images instead of automatically adding potentially poisoned labels.
- •Cloudflare Worker orchestration and a container limited to five runtime hours monthly keep the service within a £5 budget.
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