Replay-Oriented Gradient Projection Memory for Continual Learning in Medical Scenarios

Kuang Shu1; Heng Li2; Jie Cheng1; Qinghai Guo1; Luziwei Leng1; Jianxing Liao1; Yan Hu3; Jiang Liu2

10.1109/BIBM55620.2022.9995580

2022

978-1-6654-6820-6

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

1724-1729

6-8 Dec. 2022

Las Vegas, NV, USA

Despite the tremendous progress recently achieved by deep learning (DL) in medical image analysis, most DL models only concentrate on single data distribution, which follows the independent and identically distributed (i.i.d) assumption. However, in practice, image data distribution changes with clinical conditions, such as different scanner manufacturers, imaging settings, and statistics regions. Although one can further train the model on new data samples, updating a model with data from an unknown distribution will always result in the model’s performance degradation on the learned data, a notorious phenomenon called catastrophic forgetting. Therefore affects the applicability of DL algorithms in continuously changing clinical scenarios. In this study, we have proposed a new method to address the impact of changing distributions in continual learning scenarios and alleviate catastrophic forgetting. A gradient regularization approach is used to suppress forgetting, and a replay-oriented consistency calculation method combined with a subspace weighting strategy is proposed to improve the model plasticity further. The proposed replay-oriented gradient projection memory (RO-GPM) is evaluated on multiple fundus disease diagnosis datasets including a real-world application and a continual learning benchmark. The quantitative and visualization results demonstrate that the proposed RO-GPM achieves superior performance to state-of-the-art algorithms by a large margin.1

Continual learning catastrophic forgetting gradient regularization replay strategy multiple fundus disease

其他

IEEE

1.Huawei Technology Co. Ltd
2.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology
3.Department of Computer Science and Engineering, Southern University of Science and Technology

Kuang Shu,Heng Li,Jie Cheng,et al. Replay-Oriented Gradient Projection Memory for Continual Learning in Medical Scenarios[C],2022:1724-1729.