Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …

Today's deep learning methods focus on how to design the most appropriate objective
functions so that the prediction results of the model can be closest to the ground truth …

In this paper, we propose a conceptually simple but very effective attention module for
Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

Change detection (CD) aims to identify surface changes from bitemporal images. In recent
years, deep learning (DL)-based methods have made substantial breakthroughs in the field …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

Recently, pure transformer-based models have shown great potentials for vision tasks such
as image classification and detection. However, the design of transformer networks is …

In this work, we present a new network design paradigm. Our goal is to help advance the
understanding of network design and discover design principles that generalize across …