Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification
This addresses the challenge of recognizing novel disease classes in medical imaging where attribute vectors are unavailable, offering an adaptable solution for real-world applications.
The paper tackles the problem of generalized zero-shot learning for medical image classification without class attribute vectors, using self-supervised learning to select anchor vectors and generate synthetic features, achieving performance matching state-of-the-art methods for natural images and outperforming others for medical images.
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.