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Semi-Supervised Deep Embedded Clustering applied to the iSeg 2017 challenge

This repository contains the code based on our extension of Deep Embedding Clustering to semi-supervised training. This method is applied to the iSeg 2017 dataset, which can be download here: http://iseg2017.web.unc.edu/.

Folders

The main code is in the Jupyter Notebook file. The file functions.py stores many utils functions to load, preprocess and save the data, and the file clustering_layer.py contains a Keras layer which is added on top of our network. The training and testing sets have to be added to the main directory in the subfolders datasets/iSeg2017/iSeg-2017-Training and datasets/iSeg2017/iSeg-2017-Testing

Libraries

The code requires the following configuration

  • jupyter == 1.0.0
  • keras == 2.1.6
  • nibabel == 2.3.1
  • python == 2.7.12
  • sckit-learn == 0.20.0
  • tensorflow == 1.3.0