Summary of Path-rag: Knowledge-guided Key Region Retrieval For Open-ended Pathology Visual Question Answering, by Awais Naeem et al.
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering
by Awais Naeem, Tianhao Li, Huang-Ru Liao, Jiawei Xu, Aby M. Mathew, Zehao Zhu, Zhen Tan, Ajay Kumar Jaiswal, Raffi A. Salibian, Ziniu Hu, Tianlong Chen, Ying Ding
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Path-RAG framework leverages HistoCartography to retrieve domain knowledge from pathology images, significantly improving performance on the Open-ended Pathology VQA (PathVQA-Open) task. By adopting a human-centered AI approach, Path-RAG selects relevant patches from pathology images, resulting in improved accuracy of LLaVA-Med from 38% to 47%, with notable gains for H&E-stained images in the PathVQA-Open dataset. The model also achieves significant improvements on longer-form question and answer pairs in ARCH-Open PubMed (32.5%) and ARCH-Open Books (30.6%) on H&E images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to analyze complex pathology images, which are important for diagnosing and treating cancer. Instead of just using deep-learning approaches, the authors suggest combining these with domain-expert knowledge about tissue structure and cell composition. This approach is called Path-RAG and it uses a technique called HistoCartography to select relevant parts of the image. The results show that this method can improve accuracy by 9% compared to previous methods. |
Keywords
» Artificial intelligence » Deep learning » Rag
