Cai, Jiabin

Wang, Ruixuan

Zhang, Jianguo

Zheng, Weishi

Zhuang, Jiaxin

English

To date, it is still an open and challenging problem for intelligent diagnosis systems to effectively learn from imbalanced data, especially with large samples of common diseases and much smaller samples of rare ones. Inspired by the process of human learning, this paper proposes a novel and effective way to embed attention into the machine learning process, particularly for learning characteristics of rare diseases. This approach does not change architectures of the original CNN classifiers and therefore can directly plug and play for any existing CNN architecture. Comprehensive experiments on a skin lesion dataset and a pneumonia chest X-ray dataset showed that paying attention to lesion regions of rare diseases during learning not only improved the classification performance on rare diseases, but also on the mean class accuracy

University of Dundee Online Publications

CARE:Class Attention to Regions of Lesion for Classification on Imbalanced Data

https://discovery.dundee.ac.uk/files/32505958/author_accepted_version_1_.pdf

CARE:Class Attention to Regions of Lesion for Classification on Imbalanced Data

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University of Dundee Online Publications

University of Dundee Online Publications