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Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.

Published in Pattern Recognition

Due to the irregular shapes,various sizes and indistinguishable boundaries between the normal and infected tissues, it is still a challenging task to accurately segment the infected lesions of COVID-19 on CT images. In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing m...

An Improved Schema of Brain-computer Interface Based on Motor Imagery with Eye Movement

Published in Journal of Physics: Conference Series

Motor imagery (MI) can induce electroencephalogram (EEG) and realize human-computer interaction, but this kind of interaction has poor robustness and low stability. To solve these problems, we improved MI paradigms with eye movement and proposed convolutional neural network classification models based on attention mechanism. We conducted a comparat...

Colon tissue image segmentation with MWSI-NET.

Published in Medical & biological engineering & computing

Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerou...

HRANet: Hierarchical region-aware network for crowd counting

Published in Applied Intelligence (Dordrecht, Netherlands)

Aiming to tackle the most intractable problems of scale variation and complex backgrounds in crowd counting, we present an innovative framework called Hierarchical Region-Aware Network (HRANet) for crowd counting in this paper, which can better focus on crowd regions to accurately predict crowd density. In our implementation, first, we design a Reg...

Non-local matching of superpixel-based deep features for color transfer

In this article, we propose a new method for matching high-resolution feature maps from CNNs using attention mechanisms. To avoid the quadratic scaling problem of all-to-all attention, this method relies on a superpixel-based pooling dimensionality reduction strategy. From this pooling, we efficiently compute nonlocal similarities between pairs of ...

Nonlinear process modeling via unidimensional convolutional neural networks with self-attention on global and local inte...

Published in ISA transactions

Nonlinear process modeling is a primary task in intelligent manufacturing, aiming at extracting high-value features from massive process data for further process analysis like process monitoring. However, it is still a challenge to develop nonlinear process models with robust representation capability for diverse process faults. From the new perspe...

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology...

Published in Computers in biology and medicine

In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interact...

Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivi...

Published in Medical image analysis

We develop a deep learning framework, spatio-temporal directed acyclic graph with attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional magnetic resonance imaging (fMRI). This ST-DAG-Att framework comprises of two neural networks, (1) spatio-temporal graph convolutional network (ST-graph-conv) to learn the spatial and...

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strateg...

Published in Neural Computing & Applications

With the development of the Internet, information on the stock market has gradually become transparent, and stock information is easy to obtain. For investors, investment performance depends on the amount of capital and effective trading strategies. The analysis tool commonly used by investors and securities analysts is technical analysis (TA). Tec...