{"type":"ScholarlyArticle","title":"Class-consistent Contrastive Learning Driven Cross-dimensional Transformer for 3D Medical Image Classification","authors":[{"name":"Qikui Zhu","slug":"qikui-zhu","given":"Qikui","family":"Zhu","affiliation":"Department of Biomedical Engineering and"},{"name":"Chuan Fu","slug":"chuan-fu","given":"Chuan","family":"Fu","affiliation":"Department of Computer Science , Chongqing university , Chongqing , China; Department of Computer and Data Science , Case Western Reserve University , OH , USA"},{"name":"Shuo Li","slug":"shuo-li","given":"Shuo","family":"Li","affiliation":"Department of Biomedical Engineering and"}],"abstract":"Transformer emerges as an active research topic in medical image analysis. Yet, three substantial challenges limit the effectiveness of both 2D and 3D Transformers  in 3D medical image classification: 1) Challenge in capturing spatial structure correlation due to the unreasonable flattening operation; 2) Challenge in burdening the high computational complexity and memory consumption due to the quadratic growth of computational complexity and memory consumption for 3D medical data; 3) Challenge in discriminative representation learning, due to data-sensitivity. To address the above challenges, a novel Cross-dimensional Transformer (CdTransformer) and a creative Class-consistent  Contrastive Learning (CcCL) are proposed. Specifically, CdTransformer consists of two novel modules: 1) Cross-dimensional Attention Module (CAM), which breaks the limitation that Transformer cannot reasonably establish spatial structure correlation when meeting 3D medical data, meanwhile, reduces the computational complexity and memory consumption. 2) Inter-dimensional Feed-forward Network (IdFN), which addresses the challenge of traditional feed-forward networks not being able to learn depth dimension information that is unique to 3D medical data. CcCL innovatively takes full advantage of the inter-class and intra-class features from the slice-distorted samples to boost Transformer in learning feature representation. CdTransformer and CcCL are validated on six 3D medical image classification tasks. Extensive experimental results demonstrate that CdTransformer outperforms state-of-the-art CNNs and Transformers on 3D  medical image classification, and CcCL enables significantly improving Transformer in discriminative representation learning.","doi":"10.24963/ijcai.2024/200","url":"https://ijcai.org/proceedings/2024/200","pdf":"https://ijcai.org/proceedings/2024/0200.pdf","markdown":"https://ijcai.org/proceedings/2024/200.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/200","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":"Computer Vision","track":"Main Track","number":200,"pages":{"first":1807,"last":1815},"keywords":["Computer Vision: CV: Biomedical image analysis","Machine Learning: ML: Classification","Computer Vision: CV: Applications","Machine Learning: ML: Adversarial machine learning"],"videos":[],"references":[{"position":1,"raw":"[Al-Shabi et al., 2019a] Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, and Maxine Tan. 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