One-page case study
Histopathologic Cancer Detection
Trained and compared CNN architectures (custom CNN, ResNet18, VGG16, EfficientNet) on 220k+ pathology slide patches. Best model achieved 0.94 AUC using transfer learning with data augmentation.
Proof Points
220k+ image patches
0.94 AUC
4 CNN families compared
Challenges
- • Handling 220k+ high-resolution .tif pathology images with limited GPU memory
- • Class imbalance between cancer and non-cancer patches
- • Overfitting on small custom CNN without regularization
Learnings
- • Transfer learning significantly outperforms training from scratch on medical imaging
- • ExponentialLR scheduler stabilizes training on noisy medical data
- • AUC is a more reliable metric than accuracy for imbalanced medical datasets
Stack
PythonPyTorchTensorFlowNumPyPandasscikit-learnOpenCV