Of the many feats achieved by artificial intelligence (AI), the ability to process images quickly and accurately has had an ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

The performance gap between unsupervised segmentation models and SAM can be significantly reduced. UnSAM not only advances the state-of-the-art in unsupervised segmentation by 10% but also achieves ...

For most of photography’s roughly 200-year history, altering a photo convincingly required either a darkroom, some Photoshop expertise, or, at minimum, a steady hand with scissors and glue. On Tuesday ...

Abstract: Accurate segmentation of Whole Slide Image (WSI) remains a significant challenge in medical image analysis. Current methods often struggle with effective feature extraction and precise ...

The best way we have found to see and copy image IDs is to browse the official Roblox store. Here, you can search, sort, and filter for anything you want. You can easily copy the code from there, but ...

This study proposes an automated tongue analysis system that combines deep learning with traditional Chinese medicine to enhance the accuracy and objectivity of tongue diagnosis. The system includes a ...

This project demonstrates using a U-Net model with PyTorch for image segmentation and denoising tasks. It effectively segments nuclei in microscopy images and removes noise from natural images.

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 ...