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.
A research team led by Prof. LIU Lizhuang from the Shanghai Advanced Research Institute of the Chinese Academy of Sciences proposed a deep learning model named TransLNP based on self-attention mechanisms, which maps the three-dimensional microstructure and biochemical properties of mRNA-LNPs to enable high-precision automated screening of LNPs.
Researchers from USTC proposed a novel design methodology for Gaussian random number generators tailored for SerDes simulation systems, making a progress in hardware GRN generation algorithms.
A research team led by Dr. LI Yingtian from the Shenzhen Institute of Advanced Technology has proposed a novel type of soft gripper based on reprogrammable bistable actuator, which allows precise control over diverse sensitivities, and offers multiple gripping modes and adjustable response speeds through straightforward reprogramming.
A research team led by Prof. LI Zhicheng from the Shenzhen Institute of Advanced Technology has proposed an integrated diagnosis model for automatic classification of adult-type diffuse gliomas directly from annotation-free standard whole-slide pathological images without requiring molecular test.
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