AIMC Topic: Deep Learning

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Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies.

The British journal of radiology
OBJECTIVES: To demonstrate the effect of an improved deep learning-based reconstruction (DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners.

Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning.

Sensors (Basel, Switzerland)
In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the int...

miRBind: A Deep Learning Method for miRNA Binding Classification.

Genes
The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To dat...

SWFT-Net: a deep learning framework for efficient fine-tuning spot weights towards adaptive proton therapy.

Physics in medicine and biology
. One critical task for adaptive proton therapy is how to perform spot weight re-tuning and reoptimize plan, both of which are time-consuming and labor intensive. We proposed a deep learning framework (SWFT-Net) to speed up such a task, a starting po...

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification.

European radiology
BACKGROUND: Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this s...

Improving accelerated MRI by deep learning with sparsified complex data.

Magnetic resonance in medicine
PURPOSE: To obtain high-quality accelerated MR images with complex-valued reconstruction from undersampled k-space data.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

NMR in biomedicine
Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-...

Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: Early revascularization of the occluded coronary artery in patients with ST elevation myocardial infarction (STEMI) has been demonstrated to decrease mortality and morbidity. Currently, physicians rely on features of electrocardiograms (EC...

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models.

Methods in enzymology
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray...