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State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, Discipline of Intelligent Instrument and Equipment, Cancer Center and Department of Breast and Thyroid Surgery, Department of Ultrasound, Xiang'an Hospital of Xiamen University, School of Medicine, Laboratory Animal Center Xiamen University, Xiamen University, Xiamen 361005, China.
With the increasing incidence of thyroid cancer worldwide and the increasing demand for surgery, the risk of parathyroid injury is also increasing, which will lead to postoperative hypoparathyroidism (HP) and hypocalcemia. In order to improve the quality of life of patients after surgery, there is an urgent need to develop a novel platform that can identify the parathyroid gland immediately during surgery. The parathyroid gland promotes the increase of blood calcium concentration by secreting parathyroid hormone (PTH).
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Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall.
View Article and Find Full Text PDFStacked Scintillators Based Multispectral X-Ray Imaging Featuring Quantum-Cutting Perovskite Scintillators With 570 nm Absorption-Emission Shift.
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View Article and Find Full Text PDFRecursive hierarchical parametric identification of Wiener-Hammerstein systems based on initial value optimization.
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Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
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