MassRobotics survey offers a snapshot of current practices, challenges, and future expectations about sensor fusion, AI ...
Meta's new image segmentation models can identify objects and people and reconstruct them in 3D - SiliconANGLE ...
Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of ...
We're announcing our newest additions to the Segment Anything Collection, SAM 3 and SAM 3D, which simplify video editing and ...
Tech Xplore on MSN
Wireless image transmission technique filters redundant data intuitively—just like a human
A new AI-driven technology developed by researchers at UNIST promises to significantly reduce data transmission loads during image transfer, paving the way for advancements in autonomous vehicles, ...
The Context-Guided Segmentation Network (CGS-Net) developed by University of Maine researchers introduces a deep learning ...
Dividing patients into groups based on how they behave towards their condition can aid understanding of the issues that affect them and improve outcomes, such as quality of life in long-term ...
Abstract: Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made significant ...
It has been widely accepted that complementary information introduced from an auxiliary modality is beneficial in medical imaging, and multi-modality segmentation often has better performance than ...
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