Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial
intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for …

Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms
contradicts this nature, rendering a significant share of their computations redundant …

In the context of modern manufacturing, digitalization, real-time monitoring, and simulation
are integral components that contribute to efficiency and energy awareness. This research …

Global covariance pooling (GCP) as an effective alternative to global average pooling has
shown good capacity to improve deep convolutional neural networks (CNNs) in a variety of …

Accurate measurement of brain structures is essential for the evaluation of neonatal brain
growth and development. The conventional methods use manual segmentation to measure …

In the realm of practical fine-grained visual classification applications rooted in deep
learning a common scenario involves training a model using a pre-existing dataset …

A recent global health crisis, COVID-19 is a significant global health crisis that has
profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies …

This article studies the generalization of neural networks (NNs) by examining how a network
changes when trained on a training sample with or without out-of-distribution (OoD) …

Deep neural networks (DNNs) have significantly advanced computer vision tasks such as
image classification, object detection, and semantic segmentation. Residual learning, a key …

星载合成孔径雷达(SAR) 系统常受到强电磁干扰而导致成像质量下降, 但现有基于图像域的干扰
抑制方法易造成图像失真, 纹理细节信息丢失等难题. 针对上述问题, 该文提出了一种基于区域 …