  This project aims to break down large pathology images into small tiles and
then cluster those tiles into distinct groups without the knowledge of true
labels, our analysis shows how difficult certain aspects of clustering tumorous
and non-tumorous cells can be and also shows that comparing the results of
different unsupervised approaches is not a trivial task. The project also
provides a software package to be used by the digital pathology community, that
uses some of the approaches developed to perform unsupervised unsupervised tile
classification, which could then be easily manually labelled.
  The project uses a mixture of techniques ranging from classical clustering
algorithms such as K-Means and Gaussian Mixture Models to more complicated
feature extraction techniques such as deep Autoencoders and Multi-loss
learning. Throughout the project, we attempt to set a benchmark for evaluation
using a few measures such as completeness scores and cluster plots.
  Throughout our results we show that Convolutional Autoencoders manages to
slightly outperform the rest of the approaches due to its powerful internal
representation learning abilities. Moreover, we show that Gaussian Mixture
models produce better results than K-Means on average due to its flexibility in
capturing different clusters. We also show the huge difference in the
difficulties of classifying different types of pathology textures.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning