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In many real world medical image classification settings we do not haveaccess to samples of all possible disease classes, while a robust system isexpected to give high performance in recognizing novel test data. We propose ageneralized 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 attributevectors which are available for natural images but not for medical images. SSLensures that the anchor vectors are representative of each class. SSL is alsoused to generate synthetic features of unseen classes. Using a simplerarchitecture, our method matches a state of the art SSL based GZSL method fornatural images and outperforms all methods for medical images. Our method isadaptable enough to accommodate class attribute vectors when they are availablefor natural images.

Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification