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 ...

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.

With more realistic images than before, GPT Image 1.5 fares reasonably well against Google's Nano Banana Pro in my testing. OpenAI still has some work to do. I’ve been writing about consumer ...

When it comes to market segmentation, I don’t see truly well-documented cases often. At a more simplistic level, we think of classic matrices such as BCG or McKinsey’s. But the real exercise of ...

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 ...

The official PyTorch implementation for "SAM-Guided Prompt Learning for Multiple Sclerosis Lesion Segmentation". MS-SAMLess is a training-time distillation framework for Multiple Sclerosis lesion ...

A research team led by Prof. WANG Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised ...

Google just dropped a major update for its AI image generation tech, enabling anyone to generate images with more accurate outcomes. In a blog post, Google revealed Gemini 2.5 Flash Image (also called ...

From all my years in research and consulting, I think I’ve learned a thing or two about marketing worth sharing. Enduring fundamentals, mostly—yet often overlooked. So, over the course of my biweekly ...

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 ...