Summary of Wolf: Wide-scope Large Language Model Framework For Cxr Understanding, by Seil Kang et al.
WoLF: Wide-scope Large Language Model Framework for CXR Understanding
by Seil Kang, Donghyun Kim, Junhyeok Kim, Hyo Kyung Lee, Seong Jae Hwang
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents WoLF, a Wide-scope Large Language Model Framework for Chest X-ray (CXR) understanding. Recent advancements in vision-language models have led to impressive performance on Visual Question Answering (VQA) and CXR report generation tasks. However, existing frameworks still possess procedural caveats. The authors introduce WoLF to address these limitations by incorporating multi-faceted patient records from Electronic Health Records (EHRs), enhancing report generation through anatomically structured knowledge, and optimizing AI-evaluation protocols for assessing model performance. Experimental results demonstrate WoLF’s superior performance on the MIMIC-CXR dataset, achieving up to +9.47%p mean score improvement in VQA and +7.3%p BLEU-1 in report generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to understand Chest X-rays better. Right now, computers can do some tasks like answering questions about the pictures or generating reports about what they see. But there are still some problems with how this works. The authors of this paper came up with a new system called WoLF that tries to fix these issues by using more information from patient records and organizing it in a way that makes sense for computers. They also developed a new way to test how well the computer systems work. Their results show that their new system is much better at doing these tasks than other systems. |
Keywords
» Artificial intelligence » Bleu » Large language model » Question answering
