618 research outputs found
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
In this paper, we present UNet++, a new, more powerful architecture for
medical image segmentation. Our architecture is essentially a deeply-supervised
encoder-decoder network where the encoder and decoder sub-networks are
connected through a series of nested, dense skip pathways. The re-designed skip
pathways aim at reducing the semantic gap between the feature maps of the
encoder and decoder sub-networks. We argue that the optimizer would deal with
an easier learning task when the feature maps from the decoder and encoder
networks are semantically similar. We have evaluated UNet++ in comparison with
U-Net and wide U-Net architectures across multiple medical image segmentation
tasks: nodule segmentation in the low-dose CT scans of chest, nuclei
segmentation in the microscopy images, liver segmentation in abdominal CT
scans, and polyp segmentation in colonoscopy videos. Our experiments
demonstrate that UNet++ with deep supervision achieves an average IoU gain of
3.9 and 3.4 points over U-Net and wide U-Net, respectively.Comment: 8 pages, 3 figures, 3 tables, accepted by 4th Deep Learning in
Medical Image Analysis (DLMIA) Worksho
Coherence assisted resonance with sub-lifetime-limited linewidth
We demonstrate a novel approach to obtain resonance linewidth below that
limited by coherence lifetime. Cross correlation between induced intensity
modulation of two lasers coupling the target resonance exhibits a narrow
spectrum. 1/30 of the lifetime-limited width was achieved in a
proof-of-principle experiment where two ground states are the target resonance
levels. Attainable linewidth is only limited by laser shot noise in principle.
Experimental results agree with an intuitive analytical model and numerical
calculations qualitatively. This technique can be easily implemented and should
be applicable to many atomic, molecular and solid state spin systems for
spectroscopy, metrology and resonance based sensing and imaging.Comment: 5 pages 5 figure
DynLight: Realize dynamic phase duration with multi-level traffic signal control
Adopting reinforcement learning (RL) for traffic signal control (TSC) is
increasingly popular, and RL has become a promising solution for traffic signal
control. However, several challenges still need to be overcome. Firstly, most
RL methods use fixed action duration and select the green phase for the next
state, which makes the phase duration less dynamic and flexible. Secondly, the
phase sequence of RL methods can be arbitrary, affecting the real-world
deployment which may require a cyclical phase structure. Lastly, the average
travel time and throughput are not fair metrics to evaluate TSC performance. To
address these challenges, we propose a multi-level traffic signal control
framework, DynLight, which uses an optimization method Max-QueueLength (M-QL)
to determine the phase and uses a deep Q-network to determine the duration of
the corresponding phase. Based on DynLight, we further propose DynLight-C which
adopts a well-trained deep Q-network of DynLight and replace M-QL with a
cyclical control policy that actuates a set of phases in fixed cyclical order
to realize cyclical phase structure. Comprehensive experiments on multiple
real-world datasets demonstrate that DynLight achieves a new state-of-the-art.
Furthermore, the deep Q-network of DynLight can learn well on determining the
phase duration and DynLight-C demonstrates high performance for deployment.Comment: 9 pages, 9 figure
Equilibrium Portfolio Selection for Smooth Ambiguity Preferences
This paper investigates the equilibrium portfolio selection for smooth
ambiguity preferences in a continuous-time market. The investor is uncertain
about the risky asset's drift term and updates the subjective belief according
to the Bayesian rule. Two versions of the verification theorem are established
and an equilibrium strategy can be decomposed into a myopic demand and two
hedging demands. When the prior is Gaussian, the closed-form equilibrium
solution is obtained. A puzzle in the numerical results is interpreted via an
alternative representation of the smooth ambiguity preferences
Dynamic portfolio selection for nonlinear law-dependent preferences
This paper addresses the portfolio selection problem for nonlinear
law-dependent preferences in continuous time, which inherently exhibit time
inconsistency. Employing the method of stochastic maximum principle, we
establish verification theorems for equilibrium strategies, accommodating both
random market coefficients and incomplete markets. We derive the first-order
condition (FOC) for the equilibrium strategies, using a notion of functional
derivatives with respect to probability distributions. Then, with the help of
the FOC we obtain the equilibrium strategies in closed form for two classes of
implicitly defined preferences: CRRA and CARA betweenness preferences, with
deterministic market coefficients. Finally, to show applications of our
theoretical results to problems with random market coefficients, we examine the
weighted utility. We reveal that the equilibrium strategy can be described by a
coupled system of Quadratic Backward Stochastic Differential Equations
(QBSDEs). The well-posedness of this system is generally open but is
established under the special structures of our problem
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
Cardiovascular disease (CVD) is the leading cause of mortality yet largely
preventable, but the key to prevention is to identify at-risk individuals
before adverse events. For predicting individual CVD risk, carotid intima-media
thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable,
offering several advantages over CT coronary artery calcium score. However,
each CIMT examination includes several ultrasound videos, and interpreting each
of these CIMT videos involves three operations: (1) select three end-diastolic
ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI)
in each selected frame, and (3) trace the lumen-intima interface and the
media-adventitia interface in each ROI to measure CIMT. These operations are
tedious, laborious, and time consuming, a serious limitation that hinders the
widespread utilization of CIMT in clinical practice. To overcome this
limitation, this paper presents a new system to automate CIMT video
interpretation. Our extensive experiments demonstrate that the suggested system
significantly outperforms the state-of-the-art methods. The superior
performance is attributable to our unified framework based on convolutional
neural networks (CNNs) coupled with our informative image representation and
effective post-processing of the CNN outputs, which are uniquely designed for
each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang.
Automating carotid intima-media thickness video interpretation with
convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y.
Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of
CIMT videos using convolutional neural networks. Deep Learning for Medical
Image Analysis, Academic Press, 201
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