7 papers
Locating Failure in Multi-Page Visually Rich Document Understanding Systems: An Empirical Attribution
Lewei Xu, Yihao Ding, Zihan Xu +5
Multi-page visually rich document understanding (MP-VRDU) systems can fail when evidence is not faithfully represented, not selected, or not used correctly, yet final accuracy alon…
DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
Zihan Xu, Puzhen Wu, Lawrence Chun Man Lau +4
Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned ima…
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
Yi Lin, Yihao Ding, Yonghui Wu +1
Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language M…
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Hexin Dong, Yi Lin, Pengyu Zhou +25
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on cl…
A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation
Puzhen Wu, Hexin Dong, Yi Lin +2
Radiology report generation from chest X-rays is an important task in artificial intelligence with the potential to greatly reduce radiologists' workload and shorten patient wait t…
SynDoc: A Hybrid Discriminative-Generative Framework for Enhancing Synthetic Domain-Adaptive Document Key Information Extraction
Yihao Ding, Soyeon Caren Han, Yanbei Jiang +3
Domain-specific Visually Rich Document Understanding (VRDU) presents significant challenges due to the complexity and sensitivity of documents in fields such as medicine, finance,…
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends
Yihao Ding, Siwen Luo, Yue Dai +6
Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents that contain intricate visual, textual, a…