121 research outputs found
Hexapartite steering based on a four-wave-mixing process with a spatially structured pump
Multipartite Einstein-Podolsky-Rosen (EPR) steering has been widely studied,
for realizing safer quantum communication. The steering properties of six
spatially separated beams from the four-wave-mixing process with a spatially
structured pump are investigated. Behaviors of all (1+i)/(i+1)-mode (i=1,2,3)
steerings are understandable, if the role of the corresponding relative
interaction strengths are taken into account. Moreover, stronger collective
multipartite steerings including five modes also can be obtained in our scheme,
which has potential applications in ultra-secure multiuser quantum networks
when the issue of trust is critical. By further discussing about all monogamy
relations, it is noticed that the type-IV monogamy relations, which are
naturally included in our model, are conditionally satisfied. Matrix
representation is used to express the steerings for the first time, which is
very useful to understand the monogomy relations intuitively. Different
steering properties obtained in this compact phase-insensitive scheme have
potential applications for different kinds of quantum communication tasks
MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients
Overall survival (OS) time is one of the most important evaluation indices
for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play
an important role in the study of glioma prognosis OS time. Several deep
learning-based methods are proposed for the OS time prediction on multi-modal
MRI problems. However, these methods usually fuse multi-modal information at
the beginning or at the end of the deep learning networks and lack the fusion
of features from different scales. In addition, the fusion at the end of
networks always adapts global with global (eg. fully connected after
concatenation of global average pooling output) or local with local (eg.
bilinear pooling), which loses the information of local with global. In this
paper, we propose a novel method for multi-modal OS time prediction of brain
tumor patients, which contains an improved nonlocal features fusion module
introduced on different scales. Our method obtains a relative 8.76% improvement
over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive
testing demonstrates that our method could adapt to situations with missing
modalities. The code is available at
https://github.com/TangWen920812/mmmna-net.Comment: Accepted EMBC 202
Transformer Lesion Tracker
Evaluating lesion progression and treatment response via longitudinal lesion
tracking plays a critical role in clinical practice. Automated approaches for
this task are motivated by prohibitive labor costs and time consumption when
lesion matching is done manually. Previous methods typically lack the
integration of local and global information. In this work, we propose a
transformer-based approach, termed Transformer Lesion Tracker (TLT).
Specifically, we design a Cross Attention-based Transformer (CAT) to capture
and combine both global and local information to enhance feature extraction. We
also develop a Registration-based Anatomical Attention Module (RAAM) to
introduce anatomical information to CAT so that it can focus on useful feature
knowledge. A Sparse Selection Strategy (SSS) is presented for selecting
features and reducing memory footprint in Transformer training. In addition, we
use a global regression to further improve model performance. We conduct
experiments on a public dataset to show the superiority of our method and find
that our model performance has improved the average Euclidean center error by
at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is
available at https://github.com/TangWen920812/TLT.Comment: Accepted MICCAI 202
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans
In clinical practice, anisotropic volumetric medical images with low
through-plane resolution are commonly used due to short acquisition time and
lower storage cost. Nevertheless, the coarse resolution may lead to
difficulties in medical diagnosis by either physicians or computer-aided
diagnosis algorithms. Deep learning-based volumetric super-resolution (SR)
methods are feasible ways to improve resolution, with convolutional neural
networks (CNN) at their core. Despite recent progress, these methods are
limited by inherent properties of convolution operators, which ignore content
relevance and cannot effectively model long-range dependencies. In addition,
most of the existing methods use pseudo-paired volumes for training and
evaluation, where pseudo low-resolution (LR) volumes are generated by a simple
degradation of their high-resolution (HR) counterparts. However, the domain gap
between pseudo- and real-LR volumes leads to the poor performance of these
methods in practice. In this paper, we build the first public real-paired
dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results
by re-implementing four state-of-the-art CNN-based methods. Considering the
inherent shortcoming of CNN, we also propose a transformer volumetric
super-resolution network (TVSRN) based on attention mechanisms, dispensing with
convolutions entirely. This is the first research to use a pure transformer for
CT volumetric SR. The experimental results show that TVSRN significantly
outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method
achieves a better trade-off between the image quality, the number of
parameters, and the running time. Data and code are available at
https://github.com/smilenaxx/RPLHR-CT.Comment: Accepted MICCAI 202
Submission to the Kidney Tumor Segmentation Challenge 2019
In this report, we present our method description of the submission to Kidney Tumor Segmentation Challenge 2019. In this challenge, the goal is to segment the kidney and kidney tumor from the CT scans. Our method is based on a common neural architecture U-Net variant, while we pay more attention to the preprocessing stage to better understand the kidney data and postprocessing stage to reduce false positives. The experiments and results show that our proposed methods increase the segmentation accuracy compared to the basic model
Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial
Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.
Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.
Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups.
Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017
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