This codes are an extension for the Kaggle Diabetic Retinopathy Detection competitation with the support of RAM (Regression Activation Map) to localize the ROI which contributing to the specific severities of DR. A breif stats of the performance of the model is summarized as

We heavily adopt the solution from https://github.com/sveitser/kaggle_diabetic, bravos to the author!

The example RAM generated from the neural networks on 128 and 256 pixel images area as below.

For the mild-conditioned patients, RAM learned to discover the narrowing of the retinal arteries associated with reduced retinal blood flow (Figure (d)), where the vessel shows dark red. The dysfunction of the neurons of the inner retina, followed in later stages (moderate) by changes in the function of the outer retina are captured in Figure (c), as such dysfunction protects the retina from many substances in the blood (including toxins and immune cells), leading to the leaking of blood constituents into the retinal neuropile. When the patients are round the next stage (severe), as the basement membrane of the retinal blood vessels thickens, capillaries degenerate and lose cells leading to loss of blood flow and progressive ischemia and microscopic aneurysm- s which appear as balloon-like structures jutting out from the capillary walls. RAM, as shown in Figure (b), learned to converge its focus on the border where the balloon-like structures occurs. As the disease progresses to the proliferative stage, the lack of oxygen in the retina causes fragile, new, blood vessels to grow along the retina and in the clear, gel-like vitreous humour that fills the inside of the eye. In Figure (a), RAM shows the model put its attention on the grey dots scattering around, which undoubtly demonstrate the proliferative stage. We also note that if the patient has no DR and the score predicted by the model is smaller than 0.5, then the RAM uniformly shows the dot-like focus near the pupil ((e)).

More detailed about RAM please see the CAM.ipynb and our report:

Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation.
Zhiguang Wang, Jianbo Yang
https://arxiv.org/pdf/1703.10757

Extract train/test images to data/train and data/test respectively and put the trainLabels.csv file into the data directory as well.

Install python2 dependencies via,

pip install -r requirements.txt

You need a CUDA capable GPU with at least 4GB of video memory and CUDNN installed.

If you'd like to run a deterministic variant you can use the deterministic branch. Note that the branch has its own requirements.txt file. In order to achieve determinism cuda-convnet is used for convolutions instead of cuDNN. The deterministic version increases the GPU memory requirements to 6GB and takes about twice as long to run.

The project was developed and tested on arch linux and hardware with a i7-2600k CPU, GTX 970 and 980Ti GPUs and 32 GB RAM. You probably need at least 8GB of RAM as well as up to 160 GB of harddisk space (for converted images, network parameters and extracted features) to run all the code in this repository.

A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh.

Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images.

You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

All these python scripts can be invoked with --help to display a brief help message. They are meant to be executed in the order,

• convert.py crops and resizes images
• train_nn.py trains convolutional networks
• transform.py extracts features from trained convolutional networks
• blend.py blends features, optionally blending inputs from both patient eyes

Example usage:

python convert.py --crop_size 128 --convert_directory data/train_tiny --extension tiff --directory data/train
python convert.py --crop_size 128 --convert_directory data/test_tiny --extension tiff --directory data/test

Usage: convert.py [OPTIONS]

Options:
  --directory TEXT          Directory with original images.  [default: data/train]
  --convert_directory TEXT  Where to save converted images.  [default: data/train_res]
  --test                    Convert images one by one and examine them on screen.  [default: False]
  --crop_size INTEGER       Size of converted images.  [default: 256]
  --extension TEXT          Filetype of converted images.  [default: tiff]
  --help                    Show this message and exit

Example usage:

python train_nn.py --cnf configs/c_128_5x5_32.py
python train_nn.py --cnf configs/c_512_5x5_32.py --weights_from weigts/c_256_5x5_32/weights_final.pkl

Usage: train_nn.py [OPTIONS]

Options:
  --cnf TEXT           Path or name of configuration module.  [default: configs/c_512_4x4_tiny.py]
  --weights_from TEXT  Path to initial weights file.
  --help               Show this message and exit.

Example usage:

python transform.py --cnf config/c_128_5x5_32.py --train --test --n_iter 5
python transform.py --cnf config/c_128_5x5_32.py --n_iter 5 --test_dir path/to/other/image/files
python transform.py --test_dir path/to/alternative/test/files

Usage: transform.py [OPTIONS]

Options:
  --cnf TEXT           Path or name of configuration module.  [default: configs/c_512_4x4_32.py]
  --n_iter INTEGER     Iterations for test time averaging.  [default: 1]
  --skip INTEGER       Number of test time averaging iterations to skip. [default: 0]
  --test               Extract features for test set. Ignored if --test_dir is specified.  [default: False]
  --train              Extract features for training set.  [default: False]
  --weights_from TEXT  Path to weights file.
  --test_dir TEXT      Override directory with test set images.
  --help               Show this message and exit.

Example usage:

python blend.py --per_patient # use configuration in blend.yml
python blend.py --per_patient --feature_file path/to/feature/file
python blend.py --per_patient --test_dir path/to/alternative/test/files

Usage: blend.py [OPTIONS]

Options:
  --cnf TEXT            Path or name of configuration module.  [default: configs/c_512_4x4_32.py]
  --predict             Make predictions on test set features after training. [default: False]
  --per_patient         Blend features of both patient eyes.  [default: False]
  --features_file TEXT  Read features from specified file.
  --n_iter INTEGER      Number of times to fit and average.  [default: 1]
  --blend_cnf TEXT      Blending configuration file.  [default: blend.yml]
  --test_dir TEXT       Override directory with test set images.
  --help                Show this message and exit.

• The convolutional network configuration is done via the files in the configs directory.
• To select different combinations of extracted features for blending edit blend.yml.
• To tune parameters related to blending edit blend.py directly.
• To make predictions for a different test set either
  • put the resized images into the data/test_medium directory
  • or edit the test_dir field in your config file(s) inside the configs directory
  • or pass the --test_dir /path/to/test/files argument to transform.py and blend.py