Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

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 …

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

Data labeling is often the limiting step in machine learning because it requires time from
trained experts. To address the limitation on labeled data, contrastive learning, among other …

Self-supervised contrastive representation learning offers the advantage of learning
meaningful visual representations from unlabeled medical datasets for transfer learning …

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity
to leverage unlabeled and domain-specific datasets generated by image-based plant …

State of the art techniques for creating ML models in healthcare often require large
quantities of clean, labelled data. However, many healthcare organizations lack the capacity …

Learning the ability to generalize knowledge between similar contexts is particularly
important in medical imaging as data distributions can shift substantially from one hospital to …

Introduction State-of-the-art multi-modal brain tumor segmentation methods often rely on
large quantities of manually annotated data to produce acceptable results. In settings where …

Disease diagnosis from medical images via supervised learning is usually dependent on
tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi …