Ultrasound imaging is a standard examination during pregnancy that can be
used for measuring specific biometric parameters towards prenatal diagnosis and
estimating gestational age. Fetal head circumference (HC) is one of the
significant factors to determine the fetus growth and health. In this paper, a
multi-task deep convolutional neural network is proposed for automatic
segmentation and estimation of HC ellipse by minimizing a compound cost
function composed of segmentation dice score and MSE of ellipse parameters.
Experimental results on fetus ultrasound dataset in different trimesters of
pregnancy show that the segmentation results and the extracted HC match well
with the radiologist annotations. The obtained dice scores of the fetal head
segmentation and the accuracy of HC evaluations are comparable to the
state-of-the-art.

利用多任务深度卷积神经网络，通过最小化由分割 Dice 系数和椭圆参数的均方误差构成的复合成本函数来进行自动分割和估计胎儿头围，并将分割结果和估计结果与放射科医师注释进行匹配，实验结果表明，该方法的分割和评估精度优于现有技术。

多任务深度学习的胎儿超声图像分割测量生物测量参数

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters  Using Multi-Task Deep Learning

We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is necessary to elicit a utility model separately for each new
user. Elicitation is long and tedious, particularly if the outcome space is
large and not decomposable. There are two common approaches to utility function
elicitation. The first is to base the determination of the users utility
function solely ON elicitation OF qualitative preferences.The second makes
assumptions about the form AND decomposability OF the utility function.Here we
take a different approach: we attempt TO identify the new USERs utility
function based on classification relative to a database of previously collected
utility functions. We do this by identifying clusters of utility functions that
minimize an appropriate distance measure. Having identified the clusters, we
develop a classification scheme that requires many fewer and simpler
assessments than full utility elicitation and is more robust than utility
elicitation based solely on preferences. We have tested our algorithm on a
small database of utility functions in a prenatal diagnosis domain and the
results are quite promising.

本论文研究了将分类技术应用于效用调查中，通过识别与先前收集的效用函数数据库相关的群集来确定新用户的效用函数，从而减少全面求取用户效用的困难，并且在产前诊断领域的实验结果是相当有前途的。