AIMC Topic: Deep Learning

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Unsupervised graph-level representation learning with hierarchical contrasts.

Neural networks : the official journal of the International Neural Network Society
Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative gr...

Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.

Oral oncology
OBJECTIVE: We examined a modified encoder-decoder architecture-based fully convolutional neural network, OrganNet, for simultaneous auto-segmentation of 24 organs at risk (OARs) in the head and neck, followed by validation tests and evaluation of cli...

3-Dimensional Immunostaining and Automated Deep-Learning Based Analysis of Nerve Degeneration.

International journal of molecular sciences
Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease driven by inflammation and demyelination in the brain, spinal cord, and optic nerve. Optic neuritis, characterized by inflammation and demyelination of the optic nerve, is a sympt...

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Computational and mathematical methods in medicine
BACKGROUND: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a nov...

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

European radiology
OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC).

Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R * quantification using self-gated stack-of-radial MRI.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI.

Deep learning-based internal gross target volume definition in 4D CT images of lung cancer patients.

Medical physics
BACKGROUND: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-obs...

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.

NMR in biomedicine
The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories...

Deep MCANC: A deep learning approach to multi-channel active noise control.

Neural networks : the official journal of the International Neural Network Society
Traditional multi-channel active noise control (MCANC) is based on adaptive filtering and usually uses a separate control unit for each channel. This paper introduces a deep learning based approach for multi-channel active noise control (ANC). The pr...

Using deep-learning predictions of inter-residue distances for model validation.

Acta crystallographica. Section D, Structural biology
Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the proce...