{"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/iterative-annotation-to-ease-neural-network","title":"Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis","arxiv_id":"1812.07509","date":"2018-12-18","proceeding":null,"authors":["Brendon Lutnick","Brandon Ginley","Darshana Govind","Sean D. McGarry","Peter S. LaViolette","Rabi Yacoub","Sanjay Jain","John E. Tomaszewski","Kuang-Yu Jen","Pinaki Sarder"],"abstract":"Neural networks promise to bring robust, quantitative analysis to medical\nfields, but adoption is limited by the technicalities of training these\nnetworks. To address this translation gap between medical researchers and\nneural networks in the field of pathology, we have created an intuitive\ninterface which utilizes the commonly used whole slide image (WSI) viewer,\nAperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and\ndisplay of neural network predictions on WSIs. Leveraging this, we propose the\nuse of a human-in-the-loop strategy to reduce the burden of WSI annotation. We\ntrack network performance improvements as a function of iteration and quantify\nthe use of this pipeline for the segmentation of renal histologic findings on\nWSIs. More specifically, we present network performance when applied to\nsegmentation of renal micro compartments, and demonstrate multi-class\nsegmentation in human and mouse renal tissue slides. Finally, to show the\nadaptability of this technique to other medical imaging fields, we demonstrate\nits ability to iteratively segment human prostate glands from radiology imaging\ndata.","url_abs":"http://arxiv.org/abs/1812.07509v1","url_pdf":"http://arxiv.org/pdf/1812.07509v1.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":"iterative-annotation-to-ease-neural-network","repo_url":"https://github.com/SarderLab/H-AI-L","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.07509","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}