Medical Image Analysis with PyTorch CNNs: A HIPAA-Compliant Approach

AI-assisted radiology reduces diagnostic errors by 11%. Here is how we built a compliant medical imaging tool.

1. CNN Model Architecture

We use a ResNet-50 backbone fine-tuned on chest X-ray datasets to detect lung nodules and fractures:

python
import torchvision.models as models

model = models.resnet50(pretrained=True)
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)

2. Grad-CAM Visualization

To help radiologists understand model predictions, we overlay Grad-CAM heatmaps showing which image regions drove the classification decision.

3. HIPAA Compliance

All patient data is encrypted at rest (AES-256) and in transit (TLS 1.3). The system runs in an isolated VPC with no internet egress, and all access is audit-logged.

Summary

Combining transfer learning with explainable AI visualization gives radiologists a trustworthy second opinion while maintaining strict healthcare compliance.