{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/path-rag-knowledge-guided-key-region","title":"Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering","arxiv_id":"2411.17073","date":"2024-11-26","proceeding":null,"authors":["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"],"abstract":"Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approaches for analyzing complex pathology images, they fall short as they often overlook the domain-expert understanding of tissue structure and cell composition. In this work, we focus on a challenging Open-ended Pathology VQA (PathVQA-Open) task and propose a novel framework named Path-RAG, which leverages HistoCartography to retrieve relevant domain knowledge from pathology images and significantly improves performance on PathVQA-Open. Admitting the complexity of pathology image analysis, Path-RAG adopts a human-centered AI approach by retrieving domain knowledge using HistoCartography to select the relevant patches from pathology images. Our experiments suggest that domain guidance can significantly boost the accuracy of LLaVA-Med from 38% to 47%, with a notable gain of 28% for H&E-stained pathology images in the PathVQA-Open dataset. For longer-form question and answer pairs, our model consistently achieves significant improvements of 32.5% in ARCH-Open PubMed and 30.6% in ARCH-Open Books on H\\&E images. Our code and dataset is available here (https://github.com/embedded-robotics/path-rag).","url_abs":"https://arxiv.org/abs/2411.17073v1","url_pdf":"https://arxiv.org/pdf/2411.17073v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"path-rag-knowledge-guided-key-region","repo_url":"https://github.com/embedded-robotics/path-rag","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prognosis","task_name":"Prognosis"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"rag","task_name":"RAG"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}