Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows
potential in providing quantitative imaging biomarkers that indirectly reflect
fetal lung maturation. However, fetal motion during the acquisition hampered
quantitative analysis of the acquired DWI data and, consequently, reliable
clinical utilization. We introduce qDWI-morph, an unsupervised
deep-neural-network architecture for motion compensated quantitative DWI (qDWI)
analysis. Our approach couples a registration sub-network with a quantitative
DWI model fitting sub-network. We simultaneously estimate the qDWI parameters
and the motion model by minimizing a bio-physically-informed loss function
integrating a registration loss and a model fitting quality loss. We
demonstrated the added-value of qDWI-morph over: 1) a baseline qDWI analysis
without motion compensation and 2) a baseline deep-learning model incorporating
registration loss solely. The qDWI-morph achieved a substantially improved
correlation with the gestational age through in-vivo qDWI analysis of fetal
lung DWI data (R-squared=0.32 vs. 0.13, 0.28). Our qDWI-morph has the potential
to enable motion-compensated quantitative analysis of DWI data and to provide
clinically feasible bio-markers for non-invasive fetal lung maturity
assessment. Our code is available at:
this https URL

使用 qDWI-morph，我们成功地开发了一种无监督深度神经网络体系结构来实现运动补偿的定量 DWI 分析，从而提供了一种可以用于无创胎肺成熟度评估的临床可行的生物标志物。

qDWI-Morph：用于胎儿肺部成熟度评估的运动补偿定量扩散加权 MRI 分析

qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI  analysis for fetal lung maturity assessment

Identifying driving styles is the task of analyzing the behavior of drivers
in order to capture variations that will serve to discriminate different
drivers from each other. This task has become a prerequisite for a variety of
applications, including usage-based insurance, driver coaching, driver action
prediction, and even in designing autonomous vehicles; because driving style
encodes essential information needed by these applications. In this paper, we
present a deep-neural-network architecture, we term D-CRNN, for building
high-fidelity representations for driving style, that combine the power of
convolutional neural networks (CNN) and recurrent neural networks (RNN). Using
CNN, we capture semantic patterns of driver behavior from trajectories (such as
a turn or a braking event). We then find temporal dependencies between these
semantic patterns using RNN to encode driving style. We demonstrate the
effectiveness of these techniques for driver identification by learning driving
style through extensive experiments conducted on several large, real-world
datasets, and comparing the results with the state-of-the-art deep-learning and
non-deep-learning solutions. These experiments also demonstrate a useful
example of bias removal, by presenting how we preprocess the input data by
sampling dissimilar trajectories for each driver to prevent spatial
memorization. Finally, this paper presents an analysis of the contribution of
different attributes for driver identification; we find that engine RPM, Speed,
and Acceleration are the best combination of features.

本文提出了一种深度神经网络结构 D-CRNN，通过结合卷积神经网络（CNN）和循环神经网络（RNN）的优势，建立了高保真度的驾驶风格表示，通过几个大型实时数据库的广泛实验和与先进的深度学习和非深度学习解决方案的比较，证明了这些技术在驾驶员识别方面的有效性。