114 research outputs found
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Skeletal bone age assessment is a common clinical practice to diagnose
endocrine and metabolic disorders in child development. In this paper, we
describe a fully automated deep learning approach to the problem of bone age
assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017.
The dataset for this competition is consisted of 12.6k radiological images of
left hand labeled by the bone age and sex of patients. Our approach utilizes
several deep learning architectures: U-Net, ResNet-50, and custom VGG-style
neural networks trained end-to-end. We use images of whole hands as well as
specific parts of a hand for both training and inference. This approach allows
us to measure importance of specific hand bones for the automated bone age
analysis. We further evaluate performance of the method in the context of
skeletal development stages. Our approach outperforms other common methods for
bone age assessment.Comment: 14 pages, 9 figure
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
In this paper, we provide a novel construction of the linear-sized spectral
sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous
constructions required running time [BSS14, Zou12], our
sparsification routine can be implemented in almost-quadratic running time
.
The fundamental conceptual novelty of our work is the leveraging of a strong
connection between sparsification and a regret minimization problem over
density matrices. This connection was known to provide an interpretation of the
randomized sparsifiers of Spielman and Srivastava [SS11] via the application of
matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we
explain how matrix MWU naturally arises as an instance of the
Follow-the-Regularized-Leader framework and generalize this approach to yield a
larger class of updates. This new class allows us to accelerate the
construction of linear-sized spectral sparsifiers, and give novel insights on
the motivation behind Batson, Spielman and Srivastava [BSS14]
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
Direct observation of split-mode exciton-polaritons in a single MoS nanotube
A single nanotube synthesized from a transition metal dichalcogenide (TMDC)
exhibits strong exciton resonances and, in addition, can support optical
whispering gallery modes. This combination is promising for observing
exciton-polaritons without an external cavity. However, traditional
energy-momentum-resolved detection methods are unsuitable for this tiny object.
Instead, we propose to use split optical modes in a twisted nanotube with the
flattened cross-section, where a gradually decreasing gap between the opposite
walls leads to a change in mode energy, similar to the effect of the barrier
width on the eigenenergies in the double-well potential. Using
micro-reflectance spectroscopy, we investigated the rich pattern of polariton
branches in single MoS tubes with both variable and constant gaps. Observed
Rabi splitting in the 40 - 60 meV range is comparable to that for a MoS
monolayer in a microcavity. Our results, based on the polariton dispersion
measurements and polariton dynamics analysis, present a single TMDC nanotube as
a perfect polaritonic structure for nanophotonics
Resource-efficient low-loss four-channel active demultiplexer for single photons
We report a design and implementation of a resource-efficient spatial
demultiplexer which produces 4 indistinguishable photons with efficiency of
39.7% per channel. Our scheme is based on a free-space storage/delay line which
accumulates 4 photons and releases them by a controlled polarization rotation
using a single Pockels cell.Comment: 8 pages, 7 figure
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