7 papers
Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
Yingshu Li, Yunyi Liu, Zhenghao Chen +5
Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (e.g., 256x256) due to the fi…
CCS: Clinical Consensus Selection for Radiology Report Generation
Xi Zhang, Yingshu Li, Zaiqiao Meng +2
Radiology report generation (RRG) is commonly formulated as a single-path generation task, where a multimodal large language model (MLLM) produces one decoded report as the final o…
A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency
Zhao Yang, Wang Huan, Li Yingshuo +2
Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially under high-noise knowledge bas…
A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation
Shaoyang Zhou, Yingshu Li, Yunyi Liu +3
Chest Xray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision la…
RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
Yingshu Li, Yunyi Liu, Lingqiao Liu +2
Evaluating automatically generated radiology reports remains a fundamental challenge due to the lack of clinically grounded, interpretable, and fine-grained metrics. Existing metho…
S-RRG-Bench: Structured Radiology Report Generation with Fine-Grained Evaluation Framework
Yingshu Li, Yunyi Liu, Zhanyu Wang +4
Radiology report generation (RRG) for diagnostic images, such as chest X-rays, plays a pivotal role in both clinical practice and AI. Traditional free-text reports suffer from redu…