{"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/evaluating-and-interpreting-caption","title":"Evaluating and interpreting caption prediction for histopathology images","arxiv_id":null,"date":"2020-07-08","proceeding":"Machine Learning for Healthcare 2020 7","authors":["Renyu Zhang","Christopher Weber","Robert Grossman","Aly A. Khan"],"abstract":"The automatic generation of captions from medical images can provide for an efficient way to annotate histopathology images with natural language descriptions. Such large-scale annotation of medical images may help facilitate image retrieval tasks and standardize clinical ontologies. In this work, we focus on developing and methodically evaluating a new caption generation framework for histopathology whole-slide images. We introduce PathCap, a deep learning multi-scale framework, to predict captions from histopathology images using multi-scale views of whole-slide images. We demonstrate that our framework outperforms a standard baseline caption model on a diverse set of human tissues and provides interpretable contextual cues for understanding predicted captions. Finally, we draw attention to a novel dataset of histopathology images with captions from the Genotype-Tissue Expression (GTEx) project, providing a valuable dataset for the machine learning and healthcare community to benchmark future caption prediction and interpretation methods.","url_abs":"https://proceedings.mlr.press/v126/zhang20b.html","url_pdf":"http://proceedings.mlr.press/v126/zhang20b/zhang20b.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":"evaluating-and-interpreting-caption","repo_url":"https://github.com/zhangrenyuuchicago/PathCap","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"caption-generation","task_name":"Caption Generation"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}