COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs

TLDR: This work introduces a first-of-its-kind generalized zero-shot learning (GZSL) framework that utilizes information from two different imaging modalities (CT and x-ray) for the diagnosis of chest radiographs and makes use of CT radiology reports to create a semantic space consisting of signatures corresponding to different chest diseases and conditions.

Abstract: Zero-shot learning, in spite of its recent popularity, remains an unexplored area for medical image analysis. We introduce a first-of-its-kind generalized zero-shot learning (GZSL) framework that utilizes information from two different imaging modalities (CT and x-ray) for the diagnosis of chest radiographs. Our model makes use of CT radiology reports to create a semantic space consisting of signatures corresponding to different chest diseases and conditions. We introduce a CrOss-Modality Semantic Embedding Ensemble (COMe-SEE) for zero-shot diagnosis of chest x-rays by relating an input x-ray to a signature in the semantic space. The ensemble, designed using a novel semantic saliency preserving autoencoder, utilizes the visual and the semantic saliency to facilitate GZSL. The use of an ensemble not only helps in dealing with noise but also makes our model useful across different datasets. Experiments on two publicly available datasets show that the proposed model can be trained using one dataset and still be applied to data from another source for zero-shot diagnosis of chest x-rays.