Trustworthy Medical Vision
Status: Project Plan & Active Development — This page outlines the technical roadmap for an upcoming medical image classification project focused on explainability, uncertainty estimation, and explanation reliability analysis. I will update this page as the project progresses through its milestones.
TL;DR
- Explainable medical image classifier using pretrained CNN backbone (ResNet/EfficientNet) with visual explanations (Grad-CAM + Score-CAM)
- Uncertainty estimation via MC Dropout and/or Deep Ensembles with calibration analysis (ECE, Brier, reliability diagrams)
- Explanation reliability analysis measuring stability under perturbations and correlation with uncertainty/correctness
- Evaluation-first methodology with fixed benchmark, deterministic splits, and rigorous failure case auditing
- Responsible AI framing with explicit limitations, disclaimers, and trust failure mode analysis
Project Overview
This project builds a medical image classification system that treats confidence and explanations as first-class outputs alongside predictions. The technical contribution is not "beating SOTA," but demonstrating responsible ML research and production-minded ML engineering by analyzing when the model should be trusted and when it should not.
Core idea: In healthcare-relevant ML, a model's accuracy can look strong while its confidence is miscalibrated and its explanations are unstable. This project treats explanations as objects of measurement, not just visuals.
Motivation
Medical decision support requires trust, interpretability, and explicit acknowledgment of uncertainty. Pure classification performance is insufficient because errors can be high-impact, data shifts are common, and explanations can be visually convincing yet wrong.
What This Project Demonstrates
ML/Research Discipline
- Transfer learning with pretrained CNNs and disciplined experimental design
- Explainability engineering with Grad-CAM/Score-CAM implementation
- Uncertainty estimation (MC Dropout/Deep Ensembles) with calibration metrics
- Evaluation beyond accuracy: reliability diagrams, ECE, Brier, selective prediction
- Explanation reliability analysis: stability metrics and failure case audits
ML Engineering
- Reproducible, config-driven pipelines with tracked artifacts
- Deterministic data splits with patient-level separation
- CLI-driven workflow for training, inference, and evaluation
What This Project Does NOT Claim
- Clinical readiness, diagnostic capability, or deployment in real care
- Fairness across all demographics unless dataset supports it
- Explanations as "ground truth" — they are assessed as signals with limitations
Planned Architecture
The system follows an evaluation-first pipeline where trust analysis is as important as prediction performance.
Medical Dataset (CheXpert / NIH ChestXray14)
↓
[Data Module: Patient-Level Splits → Train/Val/Test]
↓
[Model: Pretrained CNN + Classifier Head]
↓
[Training: Transfer Learning + Early Stopping]
↓
[Uncertainty: MC Dropout / Deep Ensembles]
↓
[Explanation: Grad-CAM + Score-CAM]
↓
[Stability Analysis: Perturbation Protocol]
↓ ↓
[Calibration Metrics] [Explanation Metrics]
- ECE - Heatmap overlap
- Brier - Rank correlation
- Reliability - COM shift
↓ ↓
[Trust Analysis: Confidence × Correctness × Stability]
↓
[Failure Case Audit + Report Generation]
Technical Scope
In Scope
- Binary medical image classification task
- Pretrained CNN backbone (ResNet50 / EfficientNet-B0/B1)
- Visual explanations: Grad-CAM + Score-CAM
- Uncertainty: MC Dropout and/or Deep Ensembles
- Reliability analysis: calibration + explanation stability + trust failure cases
Out of Scope
- Large-scale training from scratch
- Clinical validation or deployment
- Extensive hyperparameter sweeps
Target Dataset
CheXpert or NIH ChestXray14 for binary classification (e.g., "Pneumonia vs No Pneumonia").
Evaluation Methodology
Predictive Performance
- AUROC (primary), Accuracy, F1 / Precision-Recall
Calibration & Uncertainty
- ECE, Brier score, Reliability diagrams, Selective prediction curves
Trust-Focused Analysis
Create "trust quadrants" for systematic analysis:
- Correct + confident + stable explanation (ideal)
- Correct + uncertain (appropriate caution)
- Incorrect + confident (danger zone)
- Incorrect + uncertain (less dangerous)
Explanation Stability
Explanations generated under perturbations (MC Dropout runs, test-time augmentation) with stability metrics: heatmap overlap, rank correlation, center-of-mass shift.
Milestones
| Phase | Milestone | Description |
|---|---|---|
| Setup | 0 — Project Scaffold | Create repo, packaging, config system, and artifact conventions |
| Data | 1 — Dataset Ingestion | Implement dataset loader and patient-level deterministic splits |
| Model | 2 — Baseline Model | Implement pretrained backbone + head, train baseline with AUROC metrics |
| Uncertainty | 3 — Uncertainty Estimation | Add dropout strategy, implement T-pass inference and calibration metrics |
| Explainability | 4 — Visual Explanations | Generate Grad-CAM heatmaps for curated samples |
| Explainability | 5 — Second Explainability Method | Add Score-CAM and compare side-by-side |
| Analysis | 6 — Explanation Stability Analysis | Compute stability metrics and relate to uncertainty/confidence/correctness |
| Evaluation | 7 — Failure Case Audit | Curate failure examples and write report with limitations |
| Packaging | 8 — Portfolio Packaging | Polish README with motivation, architecture, and repro instructions |
Planned Repository Structure
trustworthy-medical-vision/
├── README.md, pyproject.toml, Makefile
├── configs/ (data.yaml, train.yaml, infer.yaml, explain.yaml, eval.yaml)
├── src/tmv/
│ ├── data/ (datasets.py, transforms.py, splits.py)
│ ├── models/ (backbones.py, classifier.py, uncertainty/)
│ ├── explain/ (gradcam.py, scorecam.py, utils.py)
│ ├── eval/ (metrics.py, calibration.py, stability.py)
│ └── cli/ (train.py, predict.py, explain.py, evaluate.py)
├── notebooks/ (00_sanity_check.ipynb, 01_report.ipynb)
├── docs/ (data.md, methodology.md, ethics.md)
└── artifacts/
├── splits/
└── runs/[run_id]/
├── config.yaml, checkpoints/, metrics/
├── explanations/, failure_cases/
Planned Deliverables
- Config-driven training/inference/explain/eval
- Checkpointed baseline + uncertainty-enabled variant
- Calibration evaluation + selective prediction
- Explanation generation (2 methods)
- Explanation stability analysis with metrics
- Failure case audit with curated examples
- Responsible AI docs + disclaimers
- Clean README + architecture diagram + example outputs
Responsible AI & Safety
Required Disclaimers
- Research/educational decision-support only
- Not a medical device or for diagnosis
Ethical Considerations
- Dataset bias, label noise, missing demographic metadata
- Domain shift risks and confounders
Limitations
- No external validation
- Explanations are not ground truth
- Calibration and stability are dataset-dependent
Current Status
Active Development — This project is currently in the early stages. I am working through the initial milestones and will update this page with implementation progress, results, and code repository link.
Check back for updates as the project progresses through its milestones.