Generating clinically accurate radiology reports from chest X-rays demands both precise pathology recognition and coherent medical language. However, fine-tuning large vision-language models can be computationally challenging in deployment settings. We present a lightweight framework that improves report generation fro...
Taishi Nishizawa, Ayesha Issah, Ana Fuertes-Brito et al.· IEEE/ACM International Confe...· 0 citations
The proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets, and introduces a multi-view contrastive learning method that captures semantic correspondences both among multi-view radiographs within a study and between these radiographs and their associated report, thereby improving visual repre...
Qiguang Miao, Kang Liu, Zhuoqi Ma et al.· Bioinform.· 6 citations· ⚡3
De-identification of clinical notes is critical for protecting patient privacy, yet existing approaches often struggle under real-world variation and provide limited support for auditing and error analysis. By examining the outputs of NER-based systems, we observe three recurring failure modes—false positives, false ne...
Ze Zhou, Ruo-Qian Zhang, Zhicheng Jiao et al.· IEEE/ACM International Confe...· 0 citations
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