3D medical images
PulseAugur coverage of 3D medical images — every cluster mentioning 3D medical images across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New framework improves 3D medical image self-supervised learning
Researchers have developed a new framework called \method for self-supervised learning on 3D medical images, addressing the limitations of random sub-volume sampling. This new approach, \method, prioritizes anatomical a…
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New SLIP framework slashes 3D medical image segmentation latency
Researchers have developed SLIP, a novel framework for interactive 3D medical image segmentation designed to reduce latency and improve responsiveness. SLIP decouples image encoding from prompt-guided refinement, allowi…
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New Normal Guidance technique boosts AI in 3D medical image analysis
Researchers have developed a new regularization technique called Normal Guidance for attention-based multiple instance learning (MIL) in 3D medical image classification. This method encourages learned attention distribu…