Recently, a research team developed an unsupervised domain adaptation (UDA) approach, the dual domain distribution disruption with semantics preservation (DDSP) framework, achieving high-precision ...
Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...
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 ...
Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the ...
Nuclei segmentation in histology images is an import step for identifying cells and doing analysis for problems such as disease identification and/or progression. In this effort, we focus on the lack ...
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 ...
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 ...
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