no code implementations • 21 Oct 2023 • Zexue He, Yu Wang, An Yan, Yao Liu, Eric Y. Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu
Curated datasets for healthcare are often limited due to the need of human annotations from experts.
no code implementations • 4 Oct 2023 • An Yan, Yu Wang, Yiwu Zhong, Zexue He, Petros Karypis, Zihan Wang, chengyu dong, Amilcare Gentili, Chun-Nan Hsu, Jingbo Shang, Julian McAuley
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.
no code implementations • 15 May 2023 • Zexue He, An Yan, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu
Based on our analysis, we define a disambiguation rewriting task to regenerate an input to be unambiguous while preserving information about the original content.
1 code implementation • Findings (EMNLP) 2021 • An Yan, Zexue He, Xing Lu, Jiang Du, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu
Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili, Julian McAuley
In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules.