{"type":"ScholarlyArticle","title":"Hybrid Learning System for Large-scale Medical Image Analysis","authors":[{"name":"Zehua Cheng","slug":"zehua-cheng","given":"Zehua","family":"Cheng","affiliation":"University of Oxford"},{"name":"Lianlong Wu","slug":"lianlong-wu","given":"Lianlong","family":"Wu","affiliation":"University of Oxford"}],"abstract":"Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions.","doi":"10.24963/ijcai.2022/824","url":"https://ijcai.org/proceedings/2022/824","pdf":"https://ijcai.org/proceedings/2022/0824.pdf","markdown":"https://ijcai.org/proceedings/2022/824.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/824","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":"Doctoral Consortium","track":"Doctoral Consortium","number":824,"pages":{"first":5843,"last":5844},"keywords":["Computer Vision (CV): General"],"videos":[],"references":[{"position":1,"raw":"[Du et al., 2021] Guodong Du, Jia Zhang, et al. 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