{"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/dltk-state-of-the-art-reference","title":"DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images","arxiv_id":"1711.06853","date":"2017-11-18","proceeding":null,"authors":["Nick Pawlowski","Sofia Ira Ktena","Matthew C. H. Lee","Bernhard Kainz","Daniel Rueckert","Ben Glocker","Martin Rajchl"],"abstract":"We present DLTK, a toolkit providing baseline implementations for efficient\nexperimentation with deep learning methods on biomedical images. It builds on\ntop of TensorFlow and its high modularity and easy-to-use examples allow for a\nlow-threshold access to state-of-the-art implementations for typical medical\nimaging problems. A comparison of DLTK's reference implementations of popular\nnetwork architectures for image segmentation demonstrates new top performance\non the publicly available challenge data \"Multi-Atlas Labeling Beyond the\nCranial Vault\". The average test Dice similarity coefficient of $81.5$ exceeds\nthe previously best performing CNN ($75.7$) and the accuracy of the challenge\nwinning method ($79.0$).","url_abs":"http://arxiv.org/abs/1711.06853v1","url_pdf":"http://arxiv.org/pdf/1711.06853v1.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":"dltk-state-of-the-art-reference","repo_url":"https://github.com/DLTK/DLTK","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.06853","atlas_url":"https://app.syntology.ai/?focus=1711.06853","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}