Precise estimation of fetal biometry parameters from ultrasound images is
vital for evaluating fetal growth, monitoring health, and identifying potential
complications reliably. However, the automated computerized segmentation of the
fetal head, abdomen, and femur from ultrasound images, along with the
subsequent measurement of fetal biometrics, remains challenging. In this work,
we propose a multi-task learning approach to classify the region into head,
abdomen and femur as well as estimate the associated parameters. We were able
to achieve a mean absolute error (MAE) of 1.08 mm on head circumference, 1.44
mm on abdomen circumference and 1.10 mm on femur length with a classification
accuracy of 99.91\% on a dataset of fetal Ultrasound images. To achieve this,
we leverage a weighted joint classification and segmentation loss function to
train a U-Net architecture with an added classification head. The code can be
accessed through
\href{this https URL}{\texttt{Github}

我们提出了一种多任务学习方法，通过对超声图像进行分类和分割来准确估计胎儿生物度量参数，实验结果在胎儿超声图像数据集上达到了很高的分类准确率和较小的平均绝对误差。

胎儿超声异常扫描的统一生物识别估计的多任务学习方法

Multi-Task Learning Approach for Unified Biometric Estimation from Fetal  Ultrasound Anomaly Scans

The prevalence of relation networks in computer vision is in stark contrast
to underexplored point-based methods. In this paper, we explore the
possibilities of local relation operators and survey their feasibility. We
propose a scalable and efficient module, called group relation aggregator. The
module computes a feature of a group based on the aggregation of the features
of the inner-group points weighted by geometric relations and semantic
relations. We adopt this module to design our RPNet. We further verify the
expandability of RPNet, in terms of both depth and width, on the tasks of
classification and segmentation. Surprisingly, empirical results show that
wider RPNet fits for classification, while deeper RPNet works better on
segmentation. RPNet achieves state-of-the-art for classification and
segmentation on challenging benchmarks. We also compare our local aggregator
with PointNet++, with around 30% parameters and 50% computation saving.
Finally, we conduct experiments to reveal the robustness of RPNet with regard
to rigid transformation and noises.

本研究探讨了在计算机视觉中局部关系算子的可能性，提出了一种可扩展，有效的模块 ——group relation aggregator，并将其用于设计 RPNet。实验结果表明，对于分类而言，结构较宽的 RPNet 表现更佳，而对于分割而言，较深的 RPNet 更优。