{"type":"ScholarlyArticle","title":"Enhancing Unsupervised Domain Adaptation via Semantic Similarity Constraint for Medical Image Segmentation","authors":[{"name":"Tao Hu","slug":"tao-hu","given":"Tao","family":"Hu","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China"},{"name":"Shiliang Sun","slug":"shiliang-sun","given":"Shiliang","family":"Sun","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China"},{"name":"Jing Zhao","slug":"jing-zhao","given":"Jing","family":"Zhao","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China"},{"name":"Dongyu Shi","slug":"dongyu-shi","given":"Dongyu","family":"Shi","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China"}],"abstract":"This work proposes a novel unsupervised cross-modality adaptive segmentation method for medical images to tackle the performance degradation caused by the severe domain shift when neural networks are being deployed to unseen modalities. The proposed method is an end-2-end framework, which conducts appearance transformation via a domain-shared shallow content encoder and two domain-specific decoders. The feature extracted from the encoder is enhanced to be more domain-invariant by a similarity learning task using the proposed Semantic Similarity Mining (SSM) module which has a strong help of domain adaptation. The domain-invariant latent feature is then fused into the target domain segmentation sub-network, trained using the original target domain images and the translated target images from the source domain in the framework of adversarial training. The adversarial training is effective to narrow the remaining gap between domains in semantic space after appearance alignment. Experimental results on two challenging datasets demonstrate that our method outperforms the state-of-the-art approaches.","doi":"10.24963/ijcai.2022/426","url":"https://ijcai.org/proceedings/2022/426","pdf":"https://ijcai.org/proceedings/2022/0426.pdf","markdown":"https://ijcai.org/proceedings/2022/426.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/426","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":426,"pages":{"first":3071,"last":3077},"keywords":["Computer Vision: Transfer, low-shot, semi- and un- supervised learning","Machine Learning: Multi-modal learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986628","length_sec":84,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984771","length_sec":294,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bousmalis et al., 2017] Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Kr- ishnan. 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