Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …

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 …

Although recent works in semi-supervised learning (SemiSL) have accomplished significant
success in natural image segmentation, the task of learning discriminative representations …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

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

Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from
paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is …

Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

The Integration of machine learning and traditional image processing in dentistry has
resulted in many applications like automatic teeth identification and numbering, caries …