Detecting 10,000 Cells in One Second

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


In this paper, we present a generalized distributed deep neural network architecture to detect cells in whole-slide high-resolution histopathological images, which usually hold \(10^{8}\) to \(10^{10}\) pixels. Our framework can adapt and accelerate any deep convolutional neural network pixel-wise cell detector to perform whole-slide cell detection within a reasonable time limit. We accelerate the convolutional neural network forwarding through a sparse kernel technique, eliminating almost all of the redundant computation among connected patches. Since the disk I/O becomes a bottleneck when the image size scale grows larger, we propose an asynchronous prefetching technique to diminish a large portion of the disk I/O time. An unbalanced distributed sampling strategy is proposed to enhance the scalability and communication efficiency in distributed computing. Blending advantages of the sparse kernel, asynchronous prefetching and distributed sampling techniques, our framework is able to accelerate the conventional convolutional deep learning method by nearly 10, 000 times with same accuracy. Specifically, our method detects cells in a \(10^{8}\)-pixel (\(10^4\times 10^4\)) image in 20  s (approximately 10, 000 cells per second) on a single workstation, which is an encouraging result in whole-slide imaging practice.


Convolutional Neural Network Cell Detection Original Network Deep Neural Network Histopathological Image 
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.



The authors would like to thank NVIDIA for GPU donation and the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely of the authors and do not represent or imply concurrence or endorsement by NCI.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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