Abstract: The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire.

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 ...

AI tools like Google’s Veo 3 and Runway can now create strikingly realistic video. WSJ’s Joanna Stern and Jarrard Cole put them to the test in a film made almost entirely with AI. Watch the film and ...

OpenAI is rolling out a new version of ChatGPT Images that promises better instruction-following, more precise editing, and up to 4x faster image generation speeds. The new model, dubbed GPT Image 1.5 ...

Software built into the cameras on iPhones and Android phones makes quick work of decoding QR codes. How do you do that on a laptop or desktop computer? I have a friend who calls me occasionally to ...

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 ...

This repository offers the official code of the paper "A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space". We provide both an Open-Source Version (based on ...

Abstract: In this paper, we present UISE, a unified image segmentation framework that achieves efficient performance across various segmentation tasks, eliminating the need for multiple specialized ...