This study evaluates two approaches applied to computed tomography (CT)
images of patients with abdominal aortic aneurysm: one deterministic, based on
tools of Approximation Theory, and one based on Artificial Intelligence. Both
aim to segment the basal CT images to extract the patent area of the aortic
vessel, in order to propose an alternative to nephrotoxic contrast agents for
diagnosing this pathology. While the deterministic approach employs sampling
Kantorovich operators and the theory behind, leveraging the reconstruction and
enhancement capabilities of these operators applied to images, the artificial
intelligence-based approach lays on a U-net neural network. The results
obtained from testing the two methods have been compared numerically and
visually to assess their performances, demonstrating that both models yield
accurate results.

本研究评估了应用于腹主动脉瘤患者计算机断层扫描图像的两种方法：一种基于近似理论工具的确定性方法和一种基于人工智能的方法。两种方法均旨在分割基底 CT 图像，以提取主动脉血管的患区，为诊断该病理提供尿毒症造影剂的替代方案。实验结果定量和定性地比较了这两种方法的性能，表明两种模型均能得出准确的结果。

腹主动脉瘤计算机断层扫描图像的近似方法与人工智能方法的比较

An approximation-based approach versus an AI one for the study of CT  images of abdominal aorta aneurysms

In this work, we focus on the inverse medium scattering problem (IMSP), which
aims to recover unknown scatterers based on measured scattered data. Motivated
by the efficient direct sampling method (DSM) introduced in [23], we propose a
novel direct sampling-based deep learning approach (DSM-DL)for reconstructing
inhomogeneous scatterers. In particular, we use the U-Net neural network to
learn the relation between the index functions and the true contrasts. Our
proposed DSM-DL is computationally efficient, robust to noise, easy to
implement, and able to naturally incorporate multiple measured data to achieve
high-quality reconstructions. Some representative tests are carried out with
varying numbers of incident waves and different noise levels to evaluate the
performance of the proposed method. The results demonstrate the promising
benefits of combining deep learning techniques with the DSM for IMSP.

本文提出了一种基于直接采样的深度学习方法，通过 U-Net 神经网络和多个采样数据，实现了对不均匀介质散射体的高质量重构。经过测试，在不同噪声水平下，该方法表现出很好的稳健性和高效性。

一种基于直接采样的深度学习逆向介质散射问题方法

A Direct Sampling-Based Deep Learning Approach for Inverse Medium  Scattering Problems

We propose a new framework for processing Fringe Patterns (FP). Our novel
approach builds upon the hypothesis that the denoising and normalisation of FPs
can be learned by a deep neural network if enough pairs of corrupted and ideal
FPs are provided. The main contributions of this paper are the following: (1)
We propose the use of the U-net neural network architecture for FP
normalisation tasks; (2) we propose a modification for the distribution of
weights in the U-net, called here the V-net model, which is more convenient for
reconstruction tasks, and we conduct extensive experimental evidence in which
the V-net produces high-quality results for FP filtering and normalisation. (3)
We also propose two modifications of the V-net scheme, namely, a residual
version called ResV-net and a fast operating version of the V-net, to evaluate
the potential improvements when modify our proposal. We evaluate the
performance of our methods in various scenarios: FPs corrupted with different
degrees of noise, and corrupted with different noise distributions. We compare
our methodology versus other state-of-the-art methods. The experimental results
(on both synthetic and real data) demonstrate the capabilities and potential of
this new paradigm for processing interferograms.

本研究提出了一种基于深度神经网络的新的凹凸条纹图案 FP 去噪和标准化的框架，并使用 U-net，V-net 模型对其进行模拟和改良，对不同噪声和分布的 FP 进行实验评估，证明其重要性和潜力。