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Deep Learning Based on Hierarchical Self-Attention for Finance Distress Prediction Incorporating Text.

Computational intelligence and neuroscience
To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, digitalization of lengthy but indispensable textual disclosure, such as Management Discussion and Analy...

Deep semi-supervised learning via dynamic anchor graph embedding in latent space.

Neural networks : the official journal of the International Neural Network Society
Recently, deep semi-supervised graph embedding learning has drawn much attention for its appealing performance on the data with a pre-specified graph structure, which could be predefined or empirically constructed based on given data samples. However...

Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.

IEEE transactions on medical imaging
The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, w...

DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some ...

Physics-based learning with channel attention for Fourier ptychographic microscopy.

Journal of biophotonics
Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. In FPM, low-resolution intensity images captured with angle-varying illumination are synthesized in ...

Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Neuroscience
Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key chall...

Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface.

Journal of medical engineering & technology
Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-compu...

DAR-Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images.

Computational intelligence and neuroscience
With the rapid development of deep learning and the wide usage of Unmanned Aerial Vehicles (UAVs), CNN-based algorithms of vehicle detection in aerial images have been widely studied in the past several years. As a downstream task of the general obje...

Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Direct immunofluorescence (DIF) is an important medical evaluation tool for renal pathology. In the DIF images, the deposition appearances and locations of immunoglobulin on glomeruli involve immunological characteristics o...

GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention.

Sensors (Basel, Switzerland)
We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimat...