6,648 research outputs found
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
Calcium imaging is a technique for observing neuron activity as a series of
images showing indicator fluorescence over time. Manually segmenting neurons is
time-consuming, leading to research on automated calcium imaging segmentation
(ACIS). We evaluated several deep learning models for ACIS on the Neurofinder
competition datasets and report our best model: U-Net2DS, a fully convolutional
network that operates on 2D mean summary images. U-Net2DS requires minimal
domain-specific pre/post-processing and parameter adjustment, and predictions
are made on full images at 9K images per minute. It
ranks third in the Neurofinder competition () and is the best model
to exclusively use deep learning. We also demonstrate useful segmentations on
data from outside the competition. The model's simplicity, speed, and quality
results make it a practical choice for ACIS and a strong baseline for more
complex models in the future.Comment: Accepted to 3rd Workshop on Deep Learning in Medical Image Analysis
(http://cs.adelaide.edu.au/~dlmia3/
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
writin
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
We explore the trade-offs of performing linear algebra using Apache Spark,
compared to traditional C and MPI implementations on HPC platforms. Spark is
designed for data analytics on cluster computing platforms with access to local
disks and is optimized for data-parallel tasks. We examine three widely-used
and important matrix factorizations: NMF (for physical plausability), PCA (for
its ubiquity) and CX (for data interpretability). We apply these methods to
TB-sized problems in particle physics, climate modeling and bioimaging. The
data matrices are tall-and-skinny which enable the algorithms to map
conveniently into Spark's data-parallel model. We perform scaling experiments
on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide
tuning guidance to obtain high performance
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