215 research outputs found
Brain Tumor Segmentation with Deep Neural Networks
In this paper, we present a fully automatic brain tumor segmentation method
based on Deep Neural Networks (DNNs). The proposed networks are tailored to
glioblastomas (both low and high grade) pictured in MR images. By their very
nature, these tumors can appear anywhere in the brain and have almost any kind
of shape, size, and contrast. These reasons motivate our exploration of a
machine learning solution that exploits a flexible, high capacity DNN while
being extremely efficient. Here, we give a description of different model
choices that we've found to be necessary for obtaining competitive performance.
We explore in particular different architectures based on Convolutional Neural
Networks (CNN), i.e. DNNs specifically adapted to image data.
We present a novel CNN architecture which differs from those traditionally
used in computer vision. Our CNN exploits both local features as well as more
global contextual features simultaneously. Also, different from most
traditional uses of CNNs, our networks use a final layer that is a
convolutional implementation of a fully connected layer which allows a 40 fold
speed up. We also describe a 2-phase training procedure that allows us to
tackle difficulties related to the imbalance of tumor labels. Finally, we
explore a cascade architecture in which the output of a basic CNN is treated as
an additional source of information for a subsequent CNN. Results reported on
the 2013 BRATS test dataset reveal that our architecture improves over the
currently published state-of-the-art while being over 30 times faster
Speeding up Convolutional Neural Networks with Low Rank Expansions
The focus of this paper is speeding up the evaluation of convolutional neural
networks. While delivering impressive results across a range of computer vision
and machine learning tasks, these networks are computationally demanding,
limiting their deployability. Convolutional layers generally consume the bulk
of the processing time, and so in this work we present two simple schemes for
drastically speeding up these layers. This is achieved by exploiting
cross-channel or filter redundancy to construct a low rank basis of filters
that are rank-1 in the spatial domain. Our methods are architecture agnostic,
and can be easily applied to existing CPU and GPU convolutional frameworks for
tuneable speedup performance. We demonstrate this with a real world network
designed for scene text character recognition, showing a possible 2.5x speedup
with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy,
still achieving state-of-the-art on standard benchmarks
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