Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Preserving maximal information is the basic principle of designing self-supervised learning
methodologies. To reach this goal, contrastive learning adopts an implicit way which is …

RGB-infrared person re-identification is an emerging cross-modality re-identification task,
which is very challenging due to significant modality discrepancy between RGB and infrared …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing
significant disability in patients worldwide. Manual diagnosis, segmentation, and …

Data augmentation has been actively studied for robust neural networks. Most of the recent
data augmentation methods focus on augmenting datasets during the training phase. At the …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Objective The purpose of present review paper is to introduce the reader to key directions of
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …