Hybrid Learning System for Large-scale Medical Image Analysis
Hybrid Learning System for Large-scale Medical Image Analysis
Zehua Cheng, Lianlong Wu
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5843-5844.
https://doi.org/10.24963/ijcai.2022/824
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.
Keywords:
Computer Vision (CV): General