{"type":"ScholarlyArticle","title":"A Medical Image Classification Network Based on Multi-View Consistent Momentum Contrastive Learning","authors":[{"name":"Chuangui Cao","slug":"chuangui-cao","given":"Chuangui","family":"Cao","affiliation":""},{"name":"Shifei Ding","slug":"shifei-ding","given":"Shifei","family":"Ding","affiliation":""},{"name":"Lili Guo","slug":"lili-guo","given":"Lili","family":"Guo","affiliation":""}],"abstract":"Due to variations in imaging conditions, images often exhibit discrepancies in color reproduction. Furthermore, motion-induced blur can lead to edge degradation, making color sensitivity and edge blurriness two prevalent and challenging issues in both natural image processing and medical image analysis. To address these challenges, we propose a model termed the Three-View Consistency Mo-mentum Contrastive with Sobel Operator (SVCMC). Specifically, we first design a three-view momen-tum-update architecture that employs a So-bel-augmented ResNet as the backbone. We then introduce a novel contrastive loss, referred to as the Three-View Consistency Momentum Contrastive Loss. Next, to mitigate the oscillations and slow convergence commonly observed in contrastive learning, we construct a dynamic contrastive loss function that adapts in real time over the training process. Finally, we validated the superiority of our model on two medical image datasets and one natural image dataset, where its classification ac-curacy and convergence speed significantly out-performed existing state-of-the-art contrastive models.","doi":"10.24963/ijcai.2025/82","url":"https://ijcai.org/proceedings/2025/82","pdf":"https://ijcai.org/proceedings/2025/0082.pdf","markdown":"https://ijcai.org/proceedings/2025/82.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/82","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":82,"pages":{"first":729,"last":737},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}