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pro vyhledávání: '"Kim, Yunsoo A"'
Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and develo
Externí odkaz:
http://arxiv.org/abs/2409.13321
Autor:
Wu, Jinge, Wu, Zhaolong, Li, Ruizhe, Hasan, Abul, Kim, Yunsoo, Cheung, Jason P. Y., Zhang, Teng, Wu, Honghan
This study proposes an approach for error correction in radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques. The proposed framework employs a novel internal+external retrieval mechanism to ex
Externí odkaz:
http://arxiv.org/abs/2406.15045
Autor:
Wu, Zhaolong, Hasan, Abul, Wu, Jinge, Kim, Yunsoo, Cheung, Jason P. Y., Zhang, Teng, Wu, Honghan
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 methods of few-shot In-Context Learning (ICL) augmented with Chain-of-Tho
Externí odkaz:
http://arxiv.org/abs/2406.09103
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties that
Externí odkaz:
http://arxiv.org/abs/2406.06331
Cardiologists use electrocardiograms (ECG) for the detection of arrhythmias. However, continuous monitoring of ECG signals to detect cardiac abnormal-ities requires significant time and human resources. As a result, several deep learning studies have
Externí odkaz:
http://arxiv.org/abs/2404.15333
Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between these models and radiologists has been primarily limited to input ima
Externí odkaz:
http://arxiv.org/abs/2404.02370
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 potential of realizing effective visual assistants for healthcare. However, th
Externí odkaz:
http://arxiv.org/abs/2401.05827
Autor:
Wu, Jinge, Kim, Yunsoo, Keller, Eva C., Chow, Jamie, Levine, Adam P., Pontikos, Nikolas, Ibrahim, Zina, Taylor, Paul, Williams, Michelle C., Wu, Honghan
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 an evaluation dataset from real-world radiology datasets (including X-
Externí odkaz:
http://arxiv.org/abs/2312.13103
Autor:
Kim, Yunsoo, Myung, Hyun
We propose a bidirectional consecutively connected two-pathway network (BCCN) for efficient gesture recognition. The BCCN consists of two pathways: (i) a keyframe pathway and (ii) a temporal-attention pathway. The keyframe pathway is configured using
Externí odkaz:
http://arxiv.org/abs/2112.01736
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