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Cancer Metastasis Detection via Spatially Structured Deep Network

  • Bin Kong
  • Xin Wang
  • Zhongyu Li
  • Qi SongEmail author
  • Shaoting ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)

Abstract

Metastasis detection of lymph nodes in Whole-slide Images (WSIs) plays a critical role in the diagnosis of breast cancer. Automatic metastasis detection is a challenging issue due to the large variance of their appearances and the size of WSIs. Recently, deep neural networks have been employed to detect cancer metastases by dividing the WSIs into small image patches. However, most existing works simply treat these patches independently and do not consider the structural information among them. In this paper, we propose a novel deep neural network, namely Spatially Structured Network (Spatio-Net) to tackle the metastasis detection problem in WSIs. By integrating the Convolutional Neural Network (CNN) with the 2D Long-Short Term Memory (2D-LSTM), our Spatio-Net is able to learn the appearances and spatial dependencies of image patches effectively. Specifically, the CNN encodes each image patch into a compact feature vector, and the 2D-LSTM layers provide the classification results (i.e., normal or tumor), considering its dependencies on other relevant image patches. Moreover, a new loss function is designed to constrain the structure of the output labels, which further improves the performance. Finally, the metastasis positions are obtained by locating the regions with high tumor probabilities in the resulting accurate probability map. The proposed method is validated on hundreds of WSIs, and the accuracy is significantly improved, in comparison with a state-of-the-art baseline that does not have the spatial dependency constraint.

Keywords

Structure Constraint Image Patch Hide State Convolutional Neural Network Deep Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUNC CharlotteCharlotteUSA
  2. 2.CuraCloud CorporationSeattleUSA

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