In many Asian regions, especially in China, agricultural fields are typically small, scattered, and lack of clear boundaries, which complicates effective crop distribution and agricultural analysis using remote sensing technology. Now, a research group from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, addressed this challenge with a novel dual-branch deep learning model (DBL) they developed.
A research team led by Prof. ZHANG Ze from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences developed a Hyper-sampling Imaging (HSI) technology that enhances the image quality and resolution of digital imaging system.
A research team led by LI Xuefei at the Shenzhen Institutes of Advanced Technology, collaborating with TIAN Liang’s team from the Hong Kong Baptist University, developed a deconvolution algorithm called DeSide. This algorithm, based on deep learning and publicly available scRNA-seq datasets, can accurately estimate the abundance of 16 cell types across 19 types of solid tumors.
A research group led by Prof. CAI Xinxia from the Aerospace Information Research Institute of the Chinese Academy of Sciences developed a new method for fabricating high-precision, low-curvature microelectrode arrays which are designed for recording neuronal activities in the brain's deep, small volume region.
A research team led by Prof. WANG Shanshan at the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, together with collaborators, has developed a chest X-ray vision-language foundation model, MaCo, reducing the dependency on annotations while improving both clinical efficiency and diagnostic accuracy.
A team led by Dr. XU Jinping from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, together with collaborators, proposed a multiscale transformer-based model for the end-to-end segmentation of FCD lesion from multi-channel MR images, enhancing the feature representation of lesions in global field of view.
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