{"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/prompting-vision-foundation-models-for","title":"Prompting Vision Foundation Models for Pathology Image Analysis","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["CHONG YIN","SiQi Liu","Kaiyang Zhou","Vincent Wai-Sun Wong","Pong C. Yuen"],"abstract":"    The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has raised significant public concern. Accurately identifying tissue alteration regions is crucial for the diagnosis of NAFLD but this task presents challenges in pathology image analysis particularly with small-scale datasets. Recently the paradigm shift from full fine-tuning to prompting in adapting vision foundation models has offered a new perspective for small-scale data analysis. However existing prompting methods based on task-agnostic prompts are mainly developed for generic image recognition which fall short in providing instructive cues for complex pathology images. In this paper we propose  Q uantitative  A ttribute-based  P rompting (QAP) a novel prompting method specifically for liver pathology image analysis. QAP is based on two quantitative attributes namely K-function-based spatial attributes and histogram-based morphological attributes which are aimed for quantitative assessment of tissue states. Moreover a conditional prompt generator is designed to turn these instance-specific attributes into visual prompts. Extensive experiments on three diverse tasks demonstrate that our task-specific prompting method achieves better diagnostic performance as well as better interpretability. Code is available at \\href https://github.com/7LFB/QAP  https://github.com/7LFB/QAP .    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Yin_Prompting_Vision_Foundation_Models_for_Pathology_Image_Analysis_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Yin_Prompting_Vision_Foundation_Models_for_Pathology_Image_Analysis_CVPR_2024_paper.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":"prompting-vision-foundation-models-for","repo_url":"https://github.com/7lfb/qap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}