计算机科学
深度学习
域适应
卷积神经网络
学习迁移
人工智能
Boosting(机器学习)
上下文图像分类
模式识别(心理学)
机器学习
图像(数学)
分类器(UML)
作者
Yanyang Gu,Zongyuan Ge,C. Paul Bonnington,Jun Zhou
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2020-05-01
卷期号:24 (5): 1379-1393
被引量:66
标识
DOI:10.1109/jbhi.2019.2942429
摘要
Deep learning has been used to analyze and diagnose various skin diseases through medical imaging. However, recent researches show that a well-trained deep learning model may not generalize well to data from different cohorts due to domain shift. Simple data fusion techniques such as combining disease samples from different data sources are not effective to solve this problem. In this paper, we present two methods for a novel task of cross-domain skin disease recognition. Starting from a fully supervised deep convolutional neural network classifier pre-trained on ImageNet, we explore a two-step progressive transfer learning technique by fine-tuning the network on two skin disease datasets. We then propose to adopt adversarial learning as a domain adaptation technique to perform invariant attribute translation from source to target domain in order to improve the recognition performance. In order to evaluate these two methods, we analyze generalization capability of the trained model on melanoma detection, cancer detection, and cross-modality learning tasks on two skin image datasets collected from different clinical settings and cohorts with different disease distributions. The experiments prove the effectiveness of our method in solving the domain shift problem.
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