Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images

Le Hou, Vu Nguyen, Ariel B. Kanevsky, Dimitris Samaras, Tahsin M. Kurc, Tianhao Zhao, Rajarsi R. Gupta, Yi Gao, Wenjin Chen, David Foran, Joel H. Saltz

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)188-200
Number of pages13
JournalPattern Recognition
Volume86
DOIs
StatePublished - Feb 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Convolutional neural network
  • Pathology image analysis
  • Semi-supervised learning
  • Unsupervised learning

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