AIMC Topic: Neural Networks, Computer

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Intelligent escalator passenger safety management.

Scientific reports
This article addresses an approach to intelligent safety control of passengers on escalators. The aim is to improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the co...

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.

Nature communications
Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the sam...

Photo-induced non-volatile VO phase transition for neuromorphic ultraviolet sensors.

Nature communications
In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile mul...

Synergistically segmenting choroidal layer and vessel using deep learning for choroid structure analysis.

Physics in medicine and biology
. The choroid is the most vascularized structure in the human eye, whose layer structure and vessel distribution are both critical for the physiology of the retina, and disease pathogenesis of the eye. Although some works have used graph-based method...

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Physics in medicine and biology
Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of...

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation.

IEEE transactions on medical imaging
Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in...

Multi-Task Fusion for Improving Mammography Screening Data Classification.

IEEE transactions on medical imaging
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., t...

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation.

IEEE transactions on medical imaging
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinat...

Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

IEEE transactions on medical imaging
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayes...

Research on Multiplayer Posture Estimation Technology of Sports Competition Video Based on Graph Neural Network Algorithm.

Computational intelligence and neuroscience
With the explosive growth of the number of sports videos, the traditional sports video analysis method based on manual annotation has been difficult to meet the growing demand because of its high cost and many limitations. The traditional model is us...