Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …

Semi-supervised learning is a promising method for medical image segmentation under
limited annotation. However, the model cognitive bias impairs the segmentation …

Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …

Commonsense reasoning, aiming at endowing machines with a human-like ability to make
situational presumptions, is extremely challenging to generalize. For someone who barely …

The superior performance of modern deep networks usually comes with a costly training
procedure. This paper presents a new curriculum learning approach for the efficient training …

Training deep neural networks (DNNs) with limited supervision has been a popular research
topic as it can significantly alleviate the annotation burden. Self-training has been …

In medical image analysis, we often need to build an image recognition system for a target
scenario with the access to small labeled data and abundant unlabeled data, as well as …