{"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/semi-supervised-and-task-driven-data","title":"Semi-Supervised and Task-Driven Data Augmentation","arxiv_id":"1902.05396","date":"2019-02-11","proceeding":null,"authors":["Krishna Chaitanya","Neerav Karani","Christian Baumgartner","Olivio Donati","Anton Becker","Ender Konukoglu"],"abstract":"Supervised deep learning methods for segmentation require large amounts of\nlabelled training data, without which they are prone to overfitting, not\ngeneralizing well to unseen images. In practice, obtaining a large number of\nannotations from clinical experts is expensive and time-consuming. One way to\naddress scarcity of annotated examples is data augmentation using random\nspatial and intensity transformations. Recently, it has been proposed to use\ngenerative models to synthesize realistic training examples, complementing the\nrandom augmentation. So far, these methods have yielded limited gains over the\nrandom augmentation. However, there is potential to improve the approach by (i)\nexplicitly modeling deformation fields (non-affine spatial transformation) and\nintensity transformations and (ii) leveraging unlabelled data during the\ngenerative process. With this motivation, we propose a novel task-driven data\naugmentation method where to synthesize new training examples, a generative\nnetwork explicitly models and applies deformation fields and additive intensity\nmasks on existing labelled data, modeling shape and intensity variations,\nrespectively. Crucially, the generative model is optimized to be conducive to\nthe task, in this case segmentation, and constrained to match the distribution\nof images observed from labelled and unlabelled samples. Furthermore, explicit\nmodeling of deformation fields allow synthesizing segmentation masks and images\nin exact correspondence by simply applying the generated transformation to an\ninput image and the corresponding annotation. Our experiments on cardiac\nmagnetic resonance images (MRI) showed that, for the task of segmentation in\nsmall training data scenarios, the proposed method substantially outperforms\nconventional augmentation techniques.","url_abs":"http://arxiv.org/abs/1902.05396v2","url_pdf":"http://arxiv.org/pdf/1902.05396v2.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":"semi-supervised-and-task-driven-data","repo_url":"https://github.com/krishnabits001/task_driven_data_augmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.05396","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}