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

文献类型： 会议论文

第一作者： Angshuman Paul

作者： Angshuman Paul 1 ; Thomas C. Shen 1 ; Niranjan Balachandar 1 ; Yuxing Tang 1 ; Yifan Peng 2 ; Zhiyong Lu 2 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center

2.Biomedical Text Mining Group, National Center for Biotechnology Information, National Institutes of Health

关键词： Zero-shot learning;Chest x-ray;Semantic saliency;Autoencoder;Ensemble

会议名称： Workshop on Interpretability of Machine Intelligence in Medical Image Computing

主办单位：

页码： 103-111

摘要： 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.

分类号： TP181-53

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