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xECGNet: Fine-tuning attention map within convolutional neural network to improve detection and explainability of concurrent cardiac arrhythmias.

Computer methods and programs in biomedicine
Background and objectiveDetecting abnormal patterns within an electrocardiogram (ECG) is crucial for diagnosing cardiovascular diseases. We start from two unresolved problems in applying deep-learning-based ECG classification models to clinical pract...

End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax.

Journal of neural engineering
To develop an efficient, embedded electroencephalogram (EEG) channel selection approach for deep neural networks, allowing us to match the channel selection to the target model, while avoiding the large computational burdens of wrapper approaches in ...

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.

Sensors (Basel, Switzerland)
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare ...

I Understand You: Blind 3D Human Attention Inference From the Perspective of Third-Person.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Inferring object-wise human attention in 3D space from the third-person perspective (e.g., a camera) is crucial to many visual tasks and applications, including human-robot collaboration, unmanned vehicle driving, etc. Challenges arise from classical...

Noise Correlations for Faster and More Robust Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented ...

Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure.

Medical image analysis
Accurate cardiac segmentation of multimodal images, e.g., magnetic resonance (MR), computed tomography (CT) images, plays a pivot role in auxiliary diagnoses, treatments and postoperative assessments of cardiovascular diseases. However, training a we...

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...