AIMC Topic: Deep Learning

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Living systems are smarter bots: Slime mold semiosis versus AI symbol manipulation.

Bio Systems
Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. W...

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

The Lancet. Digital health
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for...

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks.

Computational intelligence and neuroscience
The structural engineering is subject to various subjective and objective factors, the deformation is usually inevitable, the deformation monitoring data usually are nonstationary and nonlinear, and the deformation prediction is a difficult problem i...

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.

Journal of healthcare engineering
Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an effici...

Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.

PloS one
Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction...

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging.

Clinical imaging
PURPOSE: There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional gr...

Deep neural network-based generalized sidelobe canceller for dual-channel far-field speech recognition.

Neural networks : the official journal of the International Neural Network Society
The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases. However, the traditional GSC is optimized based on the...

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

Nature biomedical engineering
The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound images has been hindered by inter-grader variability and high false positive rates and by deep-learning m...

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.

Journal of chemical information and modeling
This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a s...