Related papers: Generalized Zero Shot Learning For Medical Image Classification

Generalized Zero Shot Learning For Medical Image Classification

URL: http://arxiv.org/abs/2204.01728v1
Date: Mon, 4 Apr 2022 09:30:08 GMT
Title: Generalized Zero Shot Learning For Medical Image Classification
Authors: Dwarikanath Mahapatra
Abstract summary: In many real world medical image classification settings we do not have access to samples of all possible disease classes. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) Our approach does not require class attribute vectors which are available for natural images but not for medical images.
Score: 5.6512908295414
License: http://creativecommons.org/publicdomain/zero/1.0/
Abstract: In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of different disease classes; and 2) training a feature generator. Our approach does not require class attribute vectors which are available for natural images but not for medical images. SSL ensures that the anchor vectors are representative of each class. SSL is also used to generate synthetic features of unseen classes. Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images. Our method is adaptable enough to accommodate class attribute vectors when they are available for natural images.

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