Deep-Multi-Instance-Learning

This repo contains implementation code for the deep multi-instance learning (MIL) method proposed within the dissertation entitled: Deep Multi-Instance Learning for Automated Pathology Screening in Frontal Chest Radiographs. The method is implemented within Python using Tensorflow to express model architectures and faccilitate network training.

Usage

NOTE: This implementation code has been tested on a machine runnning python 2.7 and Tensorflow 1.4. 
      However, it should run seamlessly on python 3.x distros and later versions of Tensorflow. 

1. Download an image dataset for which binary classification is suitable. For example, the Kaggle Pneumonia X-ray Collection
2. Partition the dataset into a positive and a negative class. Place the positive items into data/datasets/raw/d_1 and the negative items into data/dataset/raw/d_0
3. Prepare the data and launch MIL training by running: source launch_MIL.sh from the terminal.
4. At the conclusion of training, the resulting MIL model, as well as its performance on a test set of the data, is stored data/checkpoint/stage_2

License

Code within this repository provided under the GPLv3 License.