41 research outputs found
TernausNetV2: Fully Convolutional Network for Instance Segmentation
The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV
Sequence Heterogeneity Accelerates Protein Search for Targets on DNA
The process of protein search for specific binding sites on DNA is
fundamentally important since it marks the beginning of all major biological
processes. We present a theoretical investigation that probes the role of DNA
sequence symmetry, heterogeneity and chemical composition in the protein search
dynamics. Using a discrete-state stochastic approach with a first-passage
events analysis, which takes into account the most relevant physical-chemical
processes, a full analytical description of the search dynamics is obtained. It
is found that, contrary to existing views, the protein search is generally
faster on DNA with more heterogeneous sequences. In addition, the search
dynamics might be affected by the chemical composition near the target site.
The physical origins of these phenomena are discussed. Our results suggest that
biological processes might be effectively regulated by modifying chemical
composition, symmetry and heterogeneity of a genome.Comment: 10 pages, 5 figure
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Accurate detection and localization for angiodysplasia lesions is an
important problem in early stage diagnostics of gastrointestinal bleeding and
anemia. Gold-standard for angiodysplasia detection and localization is
performed using wireless capsule endoscopy. This pill-like device is able to
produce thousand of high enough resolution images during one passage through
gastrointestinal tract. In this paper we present our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and
Localization its further improvements over the state-of-the-art results using
several novel deep neural network architectures. It address the binary
segmentation problem, where every pixel in an image is labeled as an
angiodysplasia lesions or background. Then, we analyze connected component of
each predicted mask. Based on the analysis we developed a classifier that
predict angiodysplasia lesions (binary variable) and a detector for their
localization (center of a component). In this setting, our approach outperforms
other methods in every task subcategory for angiodysplasia detection and
localization thereby providing state-of-the-art results for these problems. The
source code for our solution is made publicly available at
https://github.com/ternaus/angiodysplasia-segmentatioComment: 12 pages, 6 figure
Mechanisms of Protein Search for Targets on DNA: Theoretical Insights
Protein-DNA interactions are critical for the successful functioning of all
natural systems. The key role in these interactions is played by processes of
protein search for specific sites on DNA. Although it has been studied for many
years, only recently microscopic aspects of these processes became more clear.
In this work, we present a review on current theoretical understanding of the
molecular mechanisms of the protein target search. A comprehensive
discrete-state stochastic method to explain the dynamics of the protein search
phenomena is introduced and explained. Our theoretical approach utilizes a
first-passage analysis and it takes into account the most relevant
physical-chemical processes. It is able to describe many fascinating features
of the protein search, including unusually high effective association rates,
high selectivity and specificity, and the robustness in the presence of
crowders and sequence heterogeneity.Comment: arXiv admin note: substantial text overlap with arXiv:1804.1011