{"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/weakly-supervised-medical-image-segmentation","title":"Learning to Segment Medical Images from Few-Shot Sparse Labels","arxiv_id":"2108.05476","date":"2021-08-12","proceeding":null,"authors":["Pedro H. T. Gama","Hugo Oliveira","Jefersson A. dos Santos"],"abstract":"In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.","url_abs":"https://arxiv.org/abs/2108.05476v2","url_pdf":"https://arxiv.org/pdf/2108.05476v2.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":"weakly-supervised-medical-image-segmentation","repo_url":"https://github.com/pedrohtg/weasel","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"maml","method_name":"MAML"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}