AIMC Topic: Attention

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IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation.

Computers in biology and medicine
Accurate segmentation of medical images plays an essential role in their analysis and has a wide range of research and application values in fields of practice such as medical research, disease diagnosis, disease analysis, and auxiliary surgery. In r...

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they us...

Prediction of Head Movement in 360-Degree Videos Using Attention Model.

Sensors (Basel, Switzerland)
In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Au...

Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism.

Computational and mathematical methods in medicine
Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and at...

Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments.

IEEE transactions on neural networks and learning systems
Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown...

Incorporation of residual attention modules into two neural networks for low-dose CT denoising.

Medical physics
PURPOSE: The low-dose computed tomography (CT) imaging can reduce the damage caused by x-ray radiation to the human body. However, low-dose CT images have a different degree of artifacts than conventional CT images, and their resolution is lower than...

Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning.

IEEE transactions on medical imaging
Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the de...

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning.

Journal of chemical information and modeling
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier mole...

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Oxidative medicine and cellular longevity
Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solv...

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials.

NeuroImage
The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field...