AI – based, domain-agnostic algorithmic module minimizes human errors in clinical analysis, while setting the stage for continued innovation and a new set of tools the Company will introduce in 2021 ...

Recently, a research team developed an unsupervised domain adaptation (UDA) approach, the dual domain distribution disruption with semantics preservation (DDSP) framework, achieving high-precision ...

Cloud-based solution advances personalized healthcare through scalable, personalized 3D solutions driven by artificial intelligence. BELFAST, Northern Ireland--(BUSINESS WIRE)--Axial3D, a leader in ...

Researchers have successfully developed the technology that can accurately segment different body organs by effectively learning medical image data used for different purposes in different hospitals, ...

Recent research (2024-2025) consistently demonstrates the advantages of integrated AI-VR training: Knowledge Acquisition: ...

Radiologists are beginning to use AI-based computer vision models to help speed up the laborious process of parsing medical scans. However, these models require large amounts of carefully labeled ...

Figure. The advantages of the DDSP framework: (a) Our strategy is to make the model domain-agnostic by exposing it to numerous diverse distributions while preserving semantic information in both ...

Medical image segmentation is one of the most important tasks in modern healthcare. Every pixel in a scan tells a story, whether it marks a healthy cell, a cancerous growth, or a vital organ boundary.

In a recent study published in Nature Methods, researchers assessed a novel method for bacterial cell segmentation named Omnipose. Breakthroughs in microscopy are extremely promising for enabling ...