期刊
PATTERN RECOGNITION
卷 86, 期 -, 页码 188-200出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.09.007
关键词
Pathology image analysis; Convolutional neural network; Unsupervised learning; Semi-supervised learning
资金
- National Cancer Institute [1U24CA180924-01A1]
- U.S. National Library of Medicine [R01LM011119-01, R01LM009239]
- Ecole CentraleSupelec Paris
We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully-supervised annotation cost. (C) 2018 Elsevier Ltd. All rights reserved.
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