Explainable Deep Learning Pipeline for Medical Imaging Diagnosis

### Academic Context & Background Deep learning architectures routinely achieve high classification accuracy in medical image screening. However, clinical and academic adoption requires transparent decision reasoning. Black-box classifications cannot be audited by radiologists or student clinicians without visual attribution. ### Challenge Requirements Student teams must build a deep learning diagnostic assistant that: 1. Analyzes anonymized MRI/CT benchmark slices for lesion localization. 2. Extracts pixel-level visual justification using Grad-CAM, Integrated Gradients, or Attention Rollout. 3. Renders high-fidelity 2D/3D slice visualizations with interactive transparency and threshold controls in the browser. ### Suggested Architecture - Neural Network: PyTorch, TorchVision, MONAI or torchvision ResNet/DenseNet - Inference Service: FastAPI with asynchronous queueing (Redis/Celery) - Frontend: WebGL / Three.js slice canvas viewer with Next.js ### Evaluation Criteria - Model convergence and localization IoU on validation splits - Latency and interactivity of Grad-CAM overlay generation - Clear technical documentation and ethical data handling protocols