AIMC Topic: Deep Learning

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Document-Level Chemical-Induced Disease Relation Extraction via Hierarchical Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the ...

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

Journal of applied clinical medical physics
OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

Deep Learning for Encrypted Traffic Classification and Unknown Data Detection.

Sensors (Basel, Switzerland)
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user act...

Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.

Journal of biomedical informatics
Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) ...

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

Nutrients
PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overco...

Designing all-pay auctions using deep learning and multi-agent simulation.

Scientific reports
We propose a multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with different prize allocations resulting in different incentives and...

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.

Scientific reports
In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presenc...

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning.

Genome medicine
Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning me...