{"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/pnp-adanet-plug-and-play-adversarial-domain","title":"PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation","arxiv_id":"1812.07907","date":"2018-12-19","proceeding":null,"authors":["Qi Dou","Cheng Ouyang","Cheng Chen","Hao Chen","Ben Glocker","Xiahai Zhuang","Pheng-Ann Heng"],"abstract":"Deep convolutional networks have demonstrated the state-of-the-art\nperformance on various medical image computing tasks. Leveraging images from\ndifferent modalities for the same analysis task holds clinical benefits.\nHowever, the generalization capability of deep models on test data with\ndifferent distributions remain as a major challenge. In this paper, we propose\nthe PnPAdaNet (plug-and-play adversarial domain adaptation network) for\nadapting segmentation networks between different modalities of medical images,\ne.g., MRI and CT. We propose to tackle the significant domain shift by aligning\nthe feature spaces of source and target domains in an unsupervised manner.\nSpecifically, a domain adaptation module flexibly replaces the early encoder\nlayers of the source network, and the higher layers are shared between domains.\nWith adversarial learning, we build two discriminators whose inputs are\nrespectively multi-level features and predicted segmentation masks. We have\nvalidated our domain adaptation method on cardiac structure segmentation in\nunpaired MRI and CT. The experimental results with comprehensive ablation\nstudies demonstrate the excellent efficacy of our proposed PnP-AdaNet.\nMoreover, we introduce a novel benchmark on the cardiac dataset for the task of\nunsupervised cross-modality domain adaptation. We will make our code and\ndatabase publicly available, aiming to promote future studies on this\nchallenging yet important research topic in medical imaging.","url_abs":"http://arxiv.org/abs/1812.07907v1","url_pdf":"http://arxiv.org/pdf/1812.07907v1.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":"pnp-adanet-plug-and-play-adversarial-domain","repo_url":"https://github.com/carrenD/Med-CMDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pnp-adanet-plug-and-play-adversarial-domain","repo_url":"https://github.com/carrenD/Medical-Cross-Modality-Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"cardiac-segmentation","task_name":"Cardiac Segmentation"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"medical-image-generation","task_name":"Medical Image Generation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.07907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.07907"}},"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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