{"type":"ScholarlyArticle","title":"Multimodal Representation Distribution Learning for Medical Image Segmentation","authors":[{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Weichao Cai","slug":"weichao-cai","given":"Weichao","family":"Cai","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Qiuping Jiang","slug":"qiuping-jiang","given":"Qiuping","family":"Jiang","affiliation":"School of Information Science and Engineering , Ningbo University"},{"name":"Zhihua Wang","slug":"zhihua-wang","given":"Zhihua","family":"Wang","affiliation":"Department of Engineering , Shenzhen MSU-BIT University"}],"abstract":"Medical image segmentation is one of the most critical tasks in medical image analysis. However, the performance of existing methods is limited by the lack of high-quality labeled data due to the expensive data annotation. To alleviate this limitation, we propose a novel multi-modal learning method for medical image segmentation. In our method, medical text annotation is incorporated to compensate for the quality deficiency in image data. Moreover, previous multi-modal fusion methods ignore the commonalities and differences between different modalities. Ideally, the fused features should maximize valuable information while minimizing redundant information. To achieve this goal, we propose a multimodal feature distribution learning method. It is adopted to model the commonalities and differences between text and image. Since medical image segmentation needs to predict detailed segmentation boundaries, we also design a prompt encoder to achieve fine-grained segmentation. Experimental results on three datasets show that the proposed method obtains superior segmentation performance. Source codes will be available at https://github.com/GPIOX/Multimodal.git.","doi":"10.24963/ijcai.2024/459","url":"https://ijcai.org/proceedings/2024/459","pdf":"https://ijcai.org/proceedings/2024/0459.pdf","markdown":"https://ijcai.org/proceedings/2024/459.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/459","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":459,"pages":{"first":4156,"last":4164},"keywords":["Machine Learning: ML: Multi-modal learning","Computer Vision: CV: Segmentation","Computer Vision: CV: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Ates et al., 2023] Gorkem Can Ates, Prasoon Mohan, and Emrah Celik. 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