Publication
Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems
Conference Article
Conference
IEEE International Symposium on Biomedical Imaging (ISBI)
Edition
13th
Pages
1225-1228
Doc link
http://dx.doi.org/10.1109/ISBI.2016.7493487
File
Authors
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Tschopp, Fabian
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Martel, Julien N.P.
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Turaga, Srinivas C.
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Cook, Matthew
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Funke, Jan
Abstract
With recent advances in high-throughput Electron Microscopy (EM) imaging it is now possible to image an entire nervous system of organisms like Drosophila melanogaster. One of the bottlenecks to reconstruct a connectome from these large volumes (≈ 100 TiB) is the pixel-wise prediction of membranes. The time it would typically take to process such a volume using a convolutional neural network (CNN) with a sliding window approach is in the order of years on a current GPU. With sliding windows, however, a lot of redundant computations are carried out. In this paper, we present an extension to the Caffe library to increase throughput by predicting many pixels at once. On a sliding window network successfully used for membrane classification, we show that our method achieves a speedup of up to 57×, maintaining identical prediction results.
Categories
computer vision.
Author keywords
semantic segmentation, convolutional neural network, electron microscopy
Scientific reference
F. Tschopp, J.N. Martel, S.C. Turaga, M. Cook and J. Funke. Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems, 13th IEEE International Symposium on Biomedical Imaging, 2016, Prague, pp. 1225-1228.
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