collaborators

8 papers

cs.CV2026

MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis

Sicheng Chen, Chad Wong, Tianyi Zhang +3

Whole Slide Image (WSI) analysis is pivotal in computational pathology, enabling cancer diagnosis by integrating morphological and architectural cues across magnifications. Multipl…

cs.CV2026

RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review

Zhaoyi Sun, Minal Jagtiani, Wen-wai Yim +4

Radiology reports for the same patient examination may contain clinically meaningful discrepancies arising from interpretation differences, reporting variability, or evolving asses…

cs.CL2025

Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches

Namu Park, Giridhar Kaushik Ramachandran, Kevin Lybarger +4

Large language models (LLMs) have shown considerable promise in clinical natural language processing, yet few domain-specific datasets exist to rigorously evaluate their performanc…

cs.CL2025

MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering

Wen-wai Yim, Asma Ben Abacha, Zixuan Yu +3

Evaluating natural language generation (NLG) systems in the medical domain presents unique challenges due to the critical demands for accuracy, relevance, and domain-specific exper…

cs.CL2025

A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination

Zhaoyi Sun, Wen-Wai Yim, Ozlem Uzuner +2

Objective: This review aims to explore the potential and challenges of using Natural Language Processing (NLP) to detect, correct, and mitigate medically inaccurate information, in…

cs.CL2025

Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions

Yujuan Fu, Ozlem Uzuner, Meliha Yetisgen +1

Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically traine…