Publications by authors named "Xikai Yang"

Glaucoma is one of the major eye diseases that leads to progressive optic nerve fiber damage and irreversible blindness, afflicting millions of individuals. Glaucoma forecast is a good solution to early screening and intervention of potential patients, which is helpful to prevent further deterioration of the disease. It leverages a series of historical fundus images of an eye and forecasts the likelihood of glaucoma occurrence in the future.

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Article Synopsis
  • Wearable verbal language servers enhance interactions between humans and machines using advanced acoustic vibration sensors that can pick up sound signals.
  • By integrating hydrogen bonding with protein micro-fibers and ionic hydrogel, the sensors achieve improved responsiveness, stability, and pressure sensitivity, with notable specs like a 0.6 ms response time and a detection limit of 0.12 Pa.
  • Utilizing a 1D convolutional neural network for signal processing, these sensors can accurately recognize acoustic signals, achieving a high accuracy rate of 98.2%, making them ideal for applications in biometric authentication and human-computer interaction.
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Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous literature. To address this issue, we propose a probabilistic prototype-based classifier that introduces uncertainty estimation into the entire pixel classification process, including probabilistic representation formulation, probabilistic pixel-prototype proximity matching, and distribution prototype update, leveraging principles from probability theory.

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Purpose: The recently proposed sparsifying transform (ST) models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in different layers. In this study, we propose a network-structured ST learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.

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Purpose: Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multilayer model learned in an unsupervised manner by combining both sparse representations and deep models.

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