Self-Supervised Generalized Zero Shot Learning for Medical Image Classification Using Novel Interpretable Saliency Maps

In many real world medical image classification settings, access to samples of all disease classes is not feasible, affecting the robustness of a system expected to have high performance in analyzing novel test data. This is a case of generalized zero shot learning (GZSL) aiming to recognize seen and unseen classes. We propose a GZSL method that uses self supervised learning (SSL) for: 1) selecting representative vectors of disease classes; and 2) synthesizing features of unseen classes. We also propose a novel approach to generate GradCAM saliency maps that highlight diseased regions with greater accuracy. We exploit information from the novel saliency maps to improve the clustering process by: 1) Enforcing the saliency maps of different classes to be different; and 2) Ensuring that clusters in the space of image and saliency features should yield class centroids having similar semantic information. This ensures the anchor vectors are representative of each class. Different from previous approaches, our proposed approach does not require class attribute vectors which are essential part of GZSL methods for natural images but are not available for medical images. Using a simple architecture the proposed method outperforms state of the art SSL based GZSL performance for natural images as well as multiple types of medical images. We also conduct many ablation studies to investigate the influence of different loss terms in our method.