Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...

Mass General Brigham researchers are betting that the next big leap in brain medicine will come from teaching artificial ...

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

Artificial intelligence (AI) is increasingly reshaping medical imaging, yet its progress depends heavily on the availability of reliable, well-curated data.

Retinal detachments can be diagnosed using a deep learning-powered fundus imaging system, offering expertise to screening sites.

Abstract: This paper presents a detailed review of recent advancements in 3D indoor scene segmentation driven by deep learning techniques. It provides an overview of existing segmentation models, ...