AIMC Topic: Neural Networks, Computer

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A Survey on Brain Effective Connectivity Network Learning.

IEEE transactions on neural networks and learning systems
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of...

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Medical & biological engineering & computing
Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the ...

An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction.

Computers in biology and medicine
X-ray Computed Tomography (CT) techniques play a vitally important role in clinical diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse-view CT reduces the impact of radioactivity on the human body through sp...

A learnable Gabor Convolution kernel for vessel segmentation.

Computers in biology and medicine
Vessel segmentation is significant for characterizing vascular diseases, receiving wide attention of researchers. The common vessel segmentation methods are mainly based on convolutional neural networks (CNNs), which have excellent feature learning c...

Role of calibration in uncertainty-based referral for deep learning.

Statistical methods in medical research
The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical e...

Explainability in Graph Neural Networks: A Taxonomic Survey.

IEEE transactions on pattern analysis and machine intelligence
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc tec...

Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

Journal of chemical information and modeling
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solu...

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning.

Physics in medicine and biology
. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, h...

Graph Flow: Cross-Layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation.

IEEE transactions on medical imaging
With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, high-performance convolutional neural networks always mean numerous parameters and high computatio...

MLF-IOSC: Multi-Level Fusion Network With Independent Operation Search Cell for Low-Dose CT Denoising.

IEEE transactions on medical imaging
Computed tomography (CT) is widely used in clinical medicine, and low-dose CT (LDCT) has become popular to reduce potential patient harm during CT acquisition. However, LDCT aggravates the problem of noise and artifacts in CT images, increasing diagn...