Hybrid Learning System for Large−scale Medical Image Analysis
Zehua Cheng and Lianlong Wu
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, semi-supervised, 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.
Book Title
Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence‚ IJCAI−ECAI 2022‚ Vienna‚ Austria‚ July 23−29‚ 2022
Month
July
Publisher
IJCAI/AAAI Press
Year
2022