AIMC Topic: Attention

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Human Eyes-Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises.

Neural computation
Humans actively observe the visual surroundings by focusing on salient objects and ignoring trivial details. However, computer vision models based on convolutional neural networks (CNN) often analyze visual input all at once through a single feedforw...

A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder's complexity, leading to a focus on multimodal analysis. Recent advancements...

The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of tri...

An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when...

[A medical visual question answering approach based on co-attention networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of "visual attention" crucial, but the modeling of "question attention" is equally significant. To facilitate ...

Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides...

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

Briefings in bioinformatics
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple...

Medical visual question answering via corresponding feature fusion combined with semantic attention.

Mathematical biosciences and engineering : MBE
Medical visual question answering (Med-VQA) aims to leverage a pre-trained artificial intelligence model to answer clinical questions raised by doctors or patients regarding radiology images. However, owing to the high professional requirements in th...

Sharing Worlds: Design of a Real-Time Attention Classifier for Robotic Therapy of ASD Children.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Joint attention is the capacity of sharing attention between two agents and an aspect of the environment, through the use of different cues, namely gaze. This capacity is of paramount importance for social skills. People with Autism Spectrum Disorder...

Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
With the increasing concern regarding the radiation exposure of patients undergoing computed tomography (CT) scans, researchers have been using deep learning techniques to improve the quality of denoised low-dose CT (LDCT) images. In this paper, a ca...