Eric Chang


2023

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KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim | Hajung Kim | Lei Ji | Seongsu Bae | Chanhwi Kim | Mujeen Sung | Hyunjae Kim | Kun Yan | Eric Chang | Jaewoo Kang
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets within the general domain before being transferred to the chest X-ray domain. Following a prominent VLM, we unify various domain-specific tasks into a simple sequence-to-sequence schema.It enables the model to effectively learn the required knowledge and skills from limited resources in the domain.Demonstrating superior performance on the benchmark datasets provided by the BioNLP shared task (Delbrouck et al., 2023), our model benefits from its training across multiple tasks and domains.With subtle techniques including ensemble and factual calibration, our system achieves first place on the RadSum23 leaderboard for the hidden test set.

2021

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Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
An Yan | Zexue He | Xing Lu | Jiang Du | Eric Chang | Amilcare Gentili | Julian McAuley | Chun-Nan Hsu
Findings of the Association for Computational Linguistics: EMNLP 2021

Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.