41,743 research outputs found
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation
Breast cancer is one of the common cancers that endanger the health of women
globally. Accurate target lesion segmentation is essential for early clinical
intervention and postoperative follow-up. Recently, many convolutional neural
networks (CNNs) have been proposed to segment breast tumors from ultrasound
images. However, the complex ultrasound pattern and the variable tumor shape
and size bring challenges to the accurate segmentation of the breast lesion.
Motivated by the selective kernel convolution, we introduce an enhanced
selective kernel convolution for breast tumor segmentation, which integrates
multiple feature map region representations and adaptively recalibrates the
weights of these feature map regions from the channel and spatial dimensions.
This region recalibration strategy enables the network to focus more on
high-contributing region features and mitigate the perturbation of less useful
regions. Finally, the enhanced selective kernel convolution is integrated into
U-net with deep supervision constraints to adaptively capture the robust
representation of breast tumors. Extensive experiments with twelve
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets demonstrate that our method has a more competitive
segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure
Partial Transfer Learning with Selective Adversarial Networks
Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets
A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces
This paper presents a stochastic behavior analysis of a kernel-based
stochastic restricted-gradient descent method. The restricted gradient gives a
steepest ascent direction within the so-called dictionary subspace. The
analysis provides the transient and steady state performance in the mean
squared error criterion. It also includes stability conditions in the mean and
mean-square sense. The present study is based on the analysis of the kernel
normalized least mean square (KNLMS) algorithm initially proposed by Chen et
al. Simulation results validate the analysis
Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions
A nonlinear channel estimator using complex Least Square Support Vector
Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long
Term Evolution (LTE) downlink under high mobility conditions. The estimation
algorithm makes use of the reference signals to estimate the total frequency
response of the highly selective multipath channel in the presence of
non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization (SRM) principle to carry out the regression estimation for
the frequency response function of the highly selective channel. The
simulations show the effectiveness of the proposed method which has good
performance and high precision to track the variations of the fading channels
compared to the conventional LS method and it is robust at high speed mobility.Comment: 11 page
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