{"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/continuous-online-adaptation-driven-by-user","title":"Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation","arxiv_id":"2503.06717","date":"2025-03-09","proceeding":null,"authors":["Wentian Xu","Ziyun Liang","Harry Anthony","Yasin Ibrahim","Felix Cohen","Guang Yang","Daniel Whitehouse","David Menon","Virginia Newcombe","Konstantinos Kamnitsas"],"abstract":"Interactive segmentation models use real-time user interactions, such as mouse clicks, as extra inputs to dynamically refine the model predictions. After model deployment, user corrections of model predictions could be used to adapt the model to the post-deployment data distribution, countering distribution-shift and enhancing reliability. Motivated by this, we introduce an online adaptation framework that enables an interactive segmentation model to continuously learn from user interaction and improve its performance on new data distributions, as it processes a sequence of test images. We introduce the Gaussian Point Loss function to train the model how to leverage user clicks, along with a two-stage online optimization method that adapts the model using the corrected predictions generated via user interactions. We demonstrate that this simple and therefore practical approach is very effective. Experiments on 5 fundus and 4 brain MRI databases demonstrate that our method outperforms existing approaches under various data distribution shifts, including segmentation of image modalities and pathologies not seen during training.","url_abs":"https://arxiv.org/abs/2503.06717v1","url_pdf":"https://arxiv.org/pdf/2503.06717v1.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":[],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"interactive-segmentation","task_name":"Interactive Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2503.06717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.06717"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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