{"type":"ScholarlyArticle","title":"MCD-CLIP: Multi-view Chest Disease Diagnosis with Disentangled CLIP","authors":[{"name":"Songyue Cai","slug":"songyue-cai","given":"Songyue","family":"Cai","affiliation":""},{"name":"Yujie Mo","slug":"yujie-mo","given":"Yujie","family":"Mo","affiliation":""},{"name":"Liang Peng","slug":"liang-peng","given":"Liang","family":"Peng","affiliation":""},{"name":"Yucheng Xie","slug":"yucheng-xie","given":"Yucheng","family":"Xie","affiliation":""},{"name":"Tao Tong","slug":"tao-tong","given":"Tao","family":"Tong","affiliation":""},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":""}],"abstract":"Pre-trained methods for multi-view chest X-ray images have demonstrated impressive performance in chest disease diagnosis, but there are still some limitations that need to  be addressed. Firstly, many pre-trained methods require full fine-tuning pre-trained models to induce significant computational resource usage and the prior knowledge destruction. Secondly, many pre-trained methods cannot efficiently balance consistency and complementarity among views, leading to information loss and performance degradation. To tackle these issues, we propose MCD-CLIP, a CLIP-based multi-view chest disease diagnosis method. It uses visual prompts and a Prompt-Aligner to align prompts across views, along with the additional text representation for efficient transfer. Moreover, we employ Adapters to disentangle the image representation, maintaining consistency and complementarity from different views. Experimental results on the chest X-ray dataset demonstrate that MCD-CLIP achieves comparable or better performance on a variety of tasks with 94.31% fewer tunable parameters compared to state-of-the-art methods. The source codes are released at https://github.com/YuzunoKawori/MCD-CLIP.","doi":"10.24963/ijcai.2025/79","url":"https://ijcai.org/proceedings/2025/79","pdf":"https://ijcai.org/proceedings/2025/0079.pdf","markdown":"https://ijcai.org/proceedings/2025/79.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/79","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":79,"pages":{"first":702,"last":710},"keywords":["Computer Vision: CV: Biomedical image analysis","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}