计算机科学
一般化
人工智能
正规化(语言学)
人工神经网络
深度学习
机器学习
领域(数学)
领域(数学分析)
医学影像学
深层神经网络
模式识别(心理学)
数学
数学分析
纯数学
作者
Haoliang Li,Yufei Wang,Renjie Wan,Shiqi Wang,Tie-Qiang Li,Alex C. Kot
出处
期刊:arXiv: Computer Vision and Pattern Recognition
日期:2020-09-27
被引量:12
摘要
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments. When trained on limited datasets, the deep neural network is lack of generalization capability, as the trained deep neural network on data within a certain distribution (e.g. the data captured by a certain device vendor or patient population) may not be able to generalize to the data with another distribution.
In this paper, we introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding with a novel linear-dependency regularization term to capture the shareable information among medical data collected from different domains. As a result, the trained neural network is expected to equip with better generalization capability to the unseen medical data. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared with state-of-the-art baselines.
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