The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification

• URL: http://arxiv.org/abs/2003.06451v2
• Date: Fri, 26 Jun 2020 18:19:25 GMT
• Title: The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification
• Authors: Marianne de Vriendt, Philip Sellars, Angelica I Aviles-Rivero
• Abstract summary: We consider the problem of classifying a medical image dataset when we have a limited amount of labels. Using semi-supervised learning, one can produce accurate classifications using a significantly reduced amount of labelled data. We propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of classifying a medical image dataset when we have a limited amounts of labels. This is very common yet challenging setting as labelled data is expensive, time consuming to collect and may require expert knowledge. The current classification go-to of deep supervised learning is unable to cope with such a problem setup. However, using semi-supervised learning, one can produce accurate classifications using a significantly reduced amount of labelled data. Therefore, semi-supervised learning is perfectly suited for medical image classification. However, there has almost been no uptake of semi-supervised methods in the medical domain. In this work, we propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches, which up to our knowledge it is the first time that an approach with minimal labels has been shown to such an unprecedented scale with medical data. We introduce the concept of hybrid models by defining a classifier as a combination between an energy-based model and a deep net. Our energy functional is built on the Dirichlet energy based on the graph p-Laplacian. Our framework includes energies based on the $\ell_1$ and $\ell_2$ norms. We then connected this energy model to a deep net to generate a much richer feature space to construct a stronger graph. Our framework can be set to be adapted to any complex dataset. We demonstrate, through extensive numerical comparisons, that our approach readily compete with fully-supervised state-of-the-art techniques for the applications of Malaria Cells, Mammograms and Chest X-ray classification whilst using only 20% of labels.

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