Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc�…

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and�…

Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in�…

There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine�…

Medical datasets and especially biobanks, often contain extensive tabular data with rich
clinical information in addition to images. In practice, clinicians typically have less data, both�…

Existing self-supervised medical image segmentation usually encounters the domain shift
problem (ie, the input distribution of pre-training is different from that of fine-tuning) and/or�…

High annotation costs are a substantial bottleneck in applying modern deep learning
architectures to clinically relevant medical use cases, substantiating the need for novel�…

OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks�…

Clinical image processing plays a significant role in healthcare systems and is a widely used
methodology of the current era. The Intracranial tumor affects children and adults as it is the�…

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�…