32 citations · 47 across the 16 of their papers we have counts for
16 papers
Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response
Daqian Shi, Xiaolei Diao, Jinge Wu +4
Timely and accurate analysis of population-level data is crucial for effective decision-making during public health emergencies such as the COVID-19 pandemic. However, the massive…
RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gaze
Yunsoo Kim, Jinge Wu, Honghan Wu
Large Vision-Language Models (LVLMs) have demonstrated promising performance in chest X-ray (CXR) analysis. To enhance human-computer interaction, several studies have incorporated…
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation
Yunsoo Kim, Jinge Wu, Su-Hwan Kim +3
Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in generating radiology reports fr…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
Minuscule Cell Detection in AS-OCT Images with Progressive Field-of-View Focusing
Boyu Chen, Ameenat L. Solebo, Daqian Shi +2
Anterior Segment Optical Coherence Tomography (AS-OCT) is an emerging imaging technique with great potential for diagnosing anterior uveitis, a vision-threatening ocular inflammato…
MedExQA: Medical Question Answering Benchmark with Multiple Explanations
Yunsoo Kim, Jinge Wu, Yusuf Abdulle +1
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. B…
Infusing clinical knowledge into tokenisers for language models
Abul Hasan, Jinge Wu, Quang Ngoc Nguyen +7
This study introduces a novel knowledge enhanced tokenisation mechanism, K-Tokeniser, for clinical text processing. Technically, at initialisation stage, K-Tokeniser populates glob…
Chain-of-Though (CoT) prompting strategies for medical error detection and correction
Zhaolong Wu, Abul Hasan, Jinge Wu +4
This paper describes our submission to the MEDIQA-CORR 2024 shared task for automatically detecting and correcting medical errors in clinical notes. We report results for three met…
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization
Jinge Wu, Abul Hasan, Honghan Wu
Radiology report summarization is a crucial task that can help doctors quickly identify clinically significant findings without the need to review detailed sections of reports. Thi…
Error Correction in Radiology Reports: A Knowledge Distillation-Based Multi-Stage Framework
Jinge Wu, Zhaolong Wu, Ruizhe Li +6
The increasing complexity and workload of clinical radiology leads to inevitable oversights and mistakes in their use as diagnostic tools, causing delayed treatments and sometimes…
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping
Jinge Wu, Hang Dong, Zexi Li +7
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valua…
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
Yunsoo Kim, Jinge Wu, Yusuf Abdulle +2
Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between…
Hallucination Benchmark in Medical Visual Question Answering
Jinge Wu, Yunsoo Kim, Honghan Wu
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potentia…
Exploring Multimodal Large Language Models for Radiology Report Error-checking
Jinge Wu, Yunsoo Kim, Eva C. Keller +7
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created…
Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets
Jinge Wu, Rowena Smith, Honghan Wu
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adol…
Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP
Jinge Wu, Rowena Smith, Honghan Wu
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adol…