Gihun Cho

CREPE: Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts

Gihun Cho, Seunghyun Jang, Hanbin Ko, Inhyeok Baek, Chang Min Park

Proceedings of EMNLP 2025, Main Conference (poster). Suzhou, China.

Six radiology-specific error categories, each regressed by its own head on a BiomedBERT encoder and summed into one score. Trained on 32k synthetic report pairs; agrees with radiologists on ReXVal at τ = 0.786 while running in 9.5 ms per pair — roughly 280× faster than an LLM judge.

ACL Anthology OpenReview Project page Code

Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays

Hanbin Ko, Rong Yang, Gihun Cho, Inhyeok Baek, Donguk Kim, Joonbeom Koo, Changi Kim, Dongheon Lee, Chang Min Park

IEEE Journal of Biomedical and Health Informatics (2026).

A bidirectional LLM text encoder adapted to chest radiograph reports, trained with masked token prediction and supervised contrastive learning over stylistically different but clinically equivalent variants of the same report, then dropped into a dual-tower vision-language framework.

DOI arXiv

SeamXSim: Seamless-textured virtual colonoscopy simulator via unpaired long-term video translation

Seunghyun Jang, Dongheon Lee, Yisak Kim, Gihun Cho, Kwang Woo Kim, Sihyun Kim, Jong Pil Im, Byeong Gwan Kim, Chang Min Park

Computers in Biology and Medicine 198, 111217 (2025).

Seamless colon textures synthesized from a single exemplar by inpainting and outpainting, then carried through an unpaired video translation stage to produce long-term colonoscopic sequences from simulation.

DOI PubMed

Evaluating Open and Closed-Source Language and Vision-Language Models for Multicenter Image-Based Diagnosis in Radiology

RSNA 2024, Cutting-Edge Research (abstract). Chicago, USA. First author, equal contribution.

How far open-weight models had closed the gap with commercial ones on differential diagnosis, given images, patient history and findings text together, measured against reader performance across multiple centers.