A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.
A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.
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DOI：
10.1038/s41598-018-24876-0
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发表时间：
2018-05-08
影响因子：
4.6
通讯作者：
Martel AL
中科院分区：
文献类型：
--
作者：
Peikari M;Salama S;Nofech-Mozes S;Martel AL
Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.
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