期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 21, 期 6, 页码 1625-1632出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2017.2691738
关键词
Convolutional neural networks; domain adaptation; epithelium-stroma classification; histopathological image analysis; transfer learning
类别
资金
- National Natural Science Foundation of China [81671766, 81301278, 61571382, 61571005, 61172179, 61103121]
- Guangdong Natural Science Foundation [2015A030313007]
- Natural Science Foundation of Fujian Province of China [2017J01126]
- Fundamental Research Funds for the Central Universities [20720160075, 20720150169]
- CCF-Tencent research fund
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our paper assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据