In the AI field, new models are being constantly released and every other week, a new AI image model comes out on top. So in this article, we have compiled a list of the best AI image generators which ...

Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability ...

+ The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. + Similar to supervised image segmentation, the proposed CNN assigns labels to ...

25 years ago, Jianbo Shi introduced Normalized Cuts (spectral clustering), a graph-theoretic approach to perceptual grouping that became a staple in unsupervised image segmentation. While the original ...

Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...

Traumatic brain injury (TBI) is a risk factor for neurodegeneration and cognitive decline, yet the underlying pathophysiologic mechanisms are incompletely understood. This gap in knowledge is in part ...

Abstract: Image superpixel segmentation has greatly benefited from the excellent feature extraction capabilities of neural networks. However, most existing neural network-based superpixel segmentation ...

Crop segmentation, the process of identifying crop regions in images, is fundamental to agricultural monitoring tasks such as yield prediction, pest detection, and growth assessment. Traditional ...

Abstract: Medical image segmentation plays a crucial role in computer-aided diagnosis and treatment planning. Unsupervised segmentation methods that can effectively leverage unlabeled data bring ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...