C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network

• URL: http://arxiv.org/abs/2310.05355v1
• Date: Mon, 9 Oct 2023 02:31:36 GMT
• Title: C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network
• Authors: Ruizhi Wang, Xiangtao Wang, Jie Zhou, Thomas Lukasiewicz, Zhenghua Xu
• Abstract summary: We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
• Score: 67.97926983664676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In clinical scenarios, multiple medical images with different views are usually generated simultaneously, and these images have high semantic consistency. However, most existing medical report generation methods only consider single-view data. The rich multi-view mutual information of medical images can help generate more accurate reports, however, the dependence of multi-view models on multi-view data in the inference stage severely limits their application in clinical practice. In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance. Therefore, we propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C^2M-DoT). Specifically, (i) a semantic-based multi-view contrastive learning medical report generation framework is adopted to utilize cross-view information to learn the semantic representation of lesions; (ii) a domain transfer network is further proposed to ensure that the multi-view report generation model can still achieve good inference performance under single-view input; (iii) meanwhile, optimization using a cross-modal consistency loss facilitates the generation of textual reports that are semantically consistent with medical images. Extensive experimental studies on two public benchmark datasets demonstrate that C^2M-DoT substantially outperforms state-of-the-art baselines in all metrics. Ablation studies also confirmed the validity and necessity of each component in C^2M-DoT.

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