Journal Article10.1016/J.CMPB.2018.04.012
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
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TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
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About: This article is published in Computer Methods and Programs in Biomedicine. The article was published on 01 Jul 2018. The article focuses on the topics: Electroencephalography & Deep learning.
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Citations
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MEMO Box: Health Assistant for Depression With Medicine Carrier and Exercise Adjustment Driven by Edge Computing
TL;DR: A health management assistant is proposed in this paper that focuses on emotion and takes smart medicine box as carrier, and emotion cognition and exercise adjustment recommendation can be realized for depression patients through physiological data, thus providing patients with empathic sports recommendations.
Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
Reyk Sayk Alemán Acuña,Eider Pereira-Montiel,Ever Augusto Torres Silva,David Andrés Montoya Arenas +3 more
TL;DR: This systematic review (2017-2022) analyzed 32 studies on natural language processing (NLP) in mental health research, finding increased NLP use in public health, particularly in detecting anxiety, depression, and grief, with supervised learning models being the most prevalent.
A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing.
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References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
•Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 07 Dec 2015
TL;DR: In this paper, a Parametric Rectified Linear Unit (PReLU) was proposed to improve model fitting with nearly zero extra computational cost and little overfitting risk, which achieved a 4.94% top-5 test error on ImageNet 2012 classification dataset.
•Posted Content
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
15.1K
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
5.7K