Cancer remains one of the leading causes of morbidity and mortality worldwide, characterized by complex genetic and molecular ...

Abstract: Semi-supervised learning (SSL) addresses the scarcity of annotated data in medical image segmentation by leveraging unlabeled samples to enhance model training. Currently, some methods ...

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical ...

MIT researchers developed an AI tool, MultiverSeg, to simplify the annotation of medical images for clinical research.

Abstract: Semi-supervised learning has proven highly effective in tackling the challenge of limited labeled training data in medical image segmentation. In general, current approaches, which rely on ...

MIT researchers developed an interactive, AI-based system that enables users to rapidly annotate areas of interest in new biomedical imaging datasets, without training a machine-learning model in ...

MIT researchers have created MultiverSeg, an artificial intelligence system that accelerates the segmentation of medical ...