Generalized Categorisation of Digital Pathology Whole Image Slides using Unsupervised Learning

• URL: http://arxiv.org/abs/2012.13955v1
• Date: Sun, 27 Dec 2020 14:38:22 GMT
• Title: Generalized Categorisation of Digital Pathology Whole Image Slides using Unsupervised Learning
• Authors: Mostafa Ibrahim, Kevin Bryson
• Abstract summary: The 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. The project uses a mixture of techniques ranging from classical clustering algorithms such as K-Means to more complicated feature extraction techniques such as deep Autoencoders and Multi-loss learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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