Medical imaging has become one of the most critical pillars of modern healthcare to provide insights into diagnosis, treatment planning, and disease management. However, the very success of imaging ...

Abstract: Deep learning models, including convolutional neural networks (CNNs) and transformer-based architectures, have achieved remarkable results in medical image segmentation tasks. However, the ...

A team at UCSF developed a multitask deep learning framework that can effectively predict Alzheimer’s disease diagnosis, cognitive scores, and future cognitive decline using only baseline MRI and ...

Deep learning has revolutionised medical image segmentation by enabling automated, high-precision delineation of anatomical structures and pathological regions across modalities such as MRI, CT and ...

Abstract: Medical image segmentation plays a pivotal role in modern healthcare, enabling accurate disease diagnosis, treatment planning, and patient monitoring by precisely delineating anatomical ...

Artificial intelligence (AI) image generators are becoming more powerful, and they usually rely on heavyweight large language models (LLMs) running in the cloud. But researchers say they've built a ...

Ultrasound guidance is widely used in lumbar regional anesthesia and chronic pain management because it provides radiation-free, portable, and real-time visualization. Among lumbar ultrasound views, ...

First 4D Radar Automatic Labelling tools using Segment Anything (SA) drivable area segmentation on camera using Deep Learning for Autonomous Vehicle. KAIST-Radar (K-Radar) (provided by 'AVELab') is a ...

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...

The Tapenade pipeline enables single-cell, whole-mount 3D quantification in dense multilayer organoids, linking spatial gene co-expression and nuclear deformation to emerging tissue-scale organization ...