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Detecting 10,000 Cells in One Second
Digital Object Identifier
MICCAI 2016. 2016 Oct 2; Volume 9901: Pages pp 676-684

Zheng Xu
Junzhou Huang


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 108 to 1010 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 108 -pixel ( 104×104 ) image in 20 s (approximately 10, 000 cells per second) on a single workstation, which is an encouraging result in whole-slide imaging practice.

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