Abstract: Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization ...

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

Satellite imaging is the technology that offer real-time geospatial information in the form of images. These images are further utilized across various applications for commercial purposes. The ...

The U-Net architecture achieves very good performance on very different biomedical segmentation applications. U-net architecture (example for 32x32 pixels in the lowest resolution) as presented in ...

Abstract: Image segmentation stands as a pivotal challenge in the realm of computer vision. Although in recent times, deep learning-based segmentation methods have emerged as front-runners in ...

Computer vision and image synthesis based on deep learning models, such as YOLO, U-Net, and Transformer, are advancing rapidly. These technologies are significantly impacting the field of neurology.

Introduction: Although medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. Despite advances ...

Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating ...

Background: Coronary artery segmentation, Lesion Identification and Measurement (CASLIM) on XRA images on X-ray angiography (XRA) are performed by cardiologists. Aims: The study, CASLIM aims to ...