Automated EEG-based screening of depression using deep convolutional neural network.

TL;DR: Early detection of depression and alcoholism disorders by EEG signal is a novel diagnostic tool that utilizes EEG signals, feature selection, and classification algorithms to diagnose and recognize depression and alcoholism disorders. The diagnostic tool achieves high classification accuracy and can be used in hospitals and clinics for fast and accurate detection of depression and alcoholism.

TL;DR: In this article , an optimal meta-method was proposed to solve the problem of super-resolution in instance segmentation models, which can be applied to any instant segmentation model, such as Mask R-CNN or YOLACT++.

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.

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.

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.

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.