5 papers
Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays
Kang Liu, Zhuoqi Ma, Siyu Liang +5
Despite recent advances in medical vision-language pretraining, existing models still struggle to capture the diagnostic workflow: radiographs are typically treated as context-agno…
PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation
Kang Liu, Zhuoqi Ma, Zikang Fang +3
Chest X-ray report generation aims to reduce radiologists' workload by automatically producing high-quality preliminary reports. A critical yet underexplored aspect of this task is…
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition
Siyu Liang, Yunan Li, Wentian Xin +4
Sign language recognition (SLR) faces fundamental challenges in creating accurate annotations due to the inherent complexity of simultaneous manual and non-manual signals. To the b…
EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge
Qiguang Miao, Kang Liu, Zhuoqi Ma +6
Radiology reports are crucial for planning treatment strategies and facilitating effective doctor-patient communication. However, the manual creation of these reports places a sign…
Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
Kang Liu, Zhuoqi Ma, Xiaolu Kang +4
Automated radiology report generation offers an effective solution to alleviate radiologists' workload. However, most existing methods focus primarily on single or fixed-view image…