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WoLF: Wide-scope Large Language Model Framework for CXR Understanding by Kang, Seil; Kim, Donghyun; Kim, Junhyeok; Lee, Hyo Kyung; Hwang, Seong Jae is a scholarly article available to read on EtoBox.

What is WoLF: Wide-scope Large Language Model Framework for CXR Understanding about?

Significant methodological strides have been made toward Chest X-ray (CXR) understanding via modern vision-language models (VLMs), demonstrating impressive Visual Question Answering (VQA) and CXR report generation abilities. However, existing CXR understanding frameworks still possess several procedural caveats. (1) Previous methods solely use CXR reports, which are insufficient for comprehensive Visual Question Answering (VQA), especially when additional health-related data like medication history and prior diagnoses are needed. (2) Previous methods use raw CXR reports, which are often arbitrarily structured. While modern language models can understand various text formats, restructuring reports for clearer, organized anatomy-based information could enhance their usefulness. (3) Current evaluation methods for CXR-VQA primarily emphasize linguistic correctness, lacking the capability to offer nuanced assessments of the generated answers. In this work, to address the aforementioned caveats, we introduce WoLF, a Wide-scope Large Language Model Framework for CXR understanding. To resolve (1), we capture multi-faceted records of patients, which are utilized for accurate diagnoses in re

Author

Kang, Seil; Kim, Donghyun; Kim, Junhyeok; Lee, Hyo Kyung; Hwang, Seong Jae

Published

2024

Language

EN