Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2928-2931. doi: 10.1109/EMBC46164.2021.9629944.
Feature matching is a crucial component of computer vision that has various applications. With the emergence of Computer-Aided Diagnosis (CAD), the need for feature matching has also emerged in the medical imaging field. In this paper, we proposed a novel algorithm using the Explainable Artificial Intelligence (XAI) [1] approach to achieve feature detection for ultrasound images based on the Deep Unfolding Super-resolution Network (USRNET). Based on the experimental results, our method shows higher interpretability and robustness than existing traditional feature extraction and matching algorithms. The proposed method provides a new insight for medical image processing, and may achieve better performance in the future with advancements of deep neural networks.
特征匹配是计算机视觉的一个关键组成部分，具有多种应用。随着计算机辅助诊断（CAD）的出现，医学成像领域也需要特征匹配。在本文中，我们提出了一种新的算法，该算法使用可解释人工智能（XAI）[1]方法，基于深度展开超分辨率网络（USRNET）实现超声图像的特征检测。基于实验结果，我们的方法比现有的传统特征提取和匹配算法具有更高的可解释性和鲁棒性。该方法为医学图像处理提供了新的思路，随着深度神经网络的发展，未来可能会取得更好的性能。
