HistopathologicCancerDetection

Histopathologic Cancer Detection in histopathologic scans of lymph node sections.

This project uses the dataset from here and for performance evaluation i made an 80/20 split of the train(folder) dataset for train/validation.

The performance is 90+% accuracy on validation set.

If you want to make a kaggle submission, you can add the remaining 20%(validation) to the training set, retrain and hopefully gain much better results. As you can see by the below graph, there is both relatively high bias and variance which means that a more complex model could also help.

Validation is the blue line, training is the orange one.

Instructions:

1. install dependencies in official/requirements.txt
2. export PYTHONPATH="$PYTHONPATH:/path/to/HistopathologicCancerDetection"
3. Download the dataset and extract train.zip under official/histopathicC/train/ and train_labels.csv.zip under official/histopathicC/
4. python3 convertTiftoJpeg.py to convert images from .tif to .jpeg format(change path inside the file)
5. python3 createDataset.py to generate tfrecords
6. python3 Simple_model.py to train the model

tensorboard --logdir="path/to/Simple_model_model" to view graphs