Histopathologic Cancer Detection using Convolutional Neural Networks with PyTorch

Kaggle Playground Competition (Link)

Project for Deep Learning at CU Boulder

Project Notes

My solution is based on Convolutional Neural Networks. The final model was selected through cross-validation. It is built upon a pre-trained ResNet34; larger sizes take much longer to train, while smaller ones yield poor results. I chose to balance the classes through augmentation: negative classes are augmented by a factor of 15, while positive ones by a factor of 10 (see DataGenerator in tools.py).

Notebooks and Files Description

• exploration.ipynb: EDA, used only to check class balance.
• crossvalidation.ipynb: Code to run cross-validation. Note that the entire process involves only a subset of the given data.
• train.ipynb: Code to train the final model. Training is performed using the full train dataset (as provided).
• inference.ipynb: Code to make predictions using the chosen model.
• tools.py: All the functions used.

Relevant Articles

• Scientific paper on exactly the same topic, using neural networks: Link
• Article on preprocessing for this competition: Link
• Article from one of the best submission authors: Link
• Code for the previous article: Link Note: Some functions have been taken from this repository; this code is quite old, and there are many outdated functions that do not work with current package versions.
• Article from another one of the best submission authors: Link
• Article about test-time augmentation: Link
• Article about test-time augmentation: Link