+ 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 ...
In a leap for clinical research, MIT researchers have created an AI-based system set to shake up how medical images are annotated, a task essential to the study of biomedical images. The new tool, ...
Abstract: Image superpixel segmentation has greatly benefited from the excellent feature extraction capabilities of neural networks. However, most existing neural network-based superpixel segmentation ...
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 ...
Introduction: Laryngeal high-speed video (HSV) is a widely used technique for diagnosing laryngeal diseases. Among various analytical approaches, segmentation of glottis regions has proven effective ...
This important work presents a self-supervised method for the segmentation of 3D cells in fluorescent microscopy images, conveniently packaged as a Napari plugin and tested on an annotated dataset.
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