Category supervised cross-modal hashing retrieval for chest X-ray and radiology reports. (March 2022)

• Record Type: Journal Article
• Authors: Zhang, Yong; Ou, Weihua; Zhang, Jiacheng; Deng, Jiaxin
• Is Part Of: Computers & electrical engineering. Volume 98(2022)
• Publication Date: 2022-03
• Subjects: Chest X-ray -- Radiology reports -- Cross-modal retrieval -- Category supervised network -- Union hashing network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107673
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the wide application of radiography, massive data of chest X-ray and the associated radiology reports have been accumulated. Cross-modal retrieval between the chest X-ray and radiology reports is useful for the level of practicing radiologists and substantial other medical settings. However, existing cross-modal retrieval methods, which are mainly designed for the natural images, are not suitable for the chest X-ray images and radiology reports. In this paper, we propose a category supervised cross-modal hashing retrieval between chest X-ray and radiology reports, which learns the cross-modal similarity using a category supervised hashing network and union hashing network. Specifically, we design a category hashing network to learn hash code for each category, then use the learned category hash code as supervised information to guide the learning of images modality hash, texts modality hash. On the other hand, we propose the union hashing network to learn the correlation between two different modalities. Comprehensive experiments have been done on the public dataset MIMIC-CXR and the results show that the proposed method is on average 6.62% better than the traditional shallow method, achieved an increase of 0.57% compared to the deep cross-modal hashing (DCMH) in term of mAP. The ablation study is also implemented and the results demonstrates effectiveness of the proposed method.