AIMC Topic: Deep Learning

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RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING.

Radiation protection dosimetry
Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences betwe...

Automatic purse-string suture skill assessment in transanal total mesorectal excision using deep learning-based video analysis.

BJS open
BACKGROUND: Purse-string suture in transanal total mesorectal excision is a key procedural step. The aims of this study were to develop an automatic skill assessment system for purse-string suture in transanal total mesorectal excision using deep lea...

[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.

A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in Myopic Children.

Translational vision science & technology
PURPOSE: To develop and validate a fully automated program for choroidal structure analysis within a 1500-µm-wide region of interest centered on the fovea (deep learning-based choroidal structure assessment program [DCAP]).

PhacoTrainer: Deep Learning for Cataract Surgical Videos to Track Surgical Tools.

Translational vision science & technology
PURPOSE: The purpose of this study was to build a deep-learning model that automatically analyzes cataract surgical videos for the locations of surgical landmarks, and to derive skill-related motion metrics.

DeepOM: single-molecule optical genome mapping via deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optic...

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons.

Translational vision science & technology
PURPOSE: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here...

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decade...

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

Bioinformatics (Oxford, England)
MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotatio...

Neurologic Dysfunction Assessment in Parkinson Disease Based on Fundus Photographs Using Deep Learning.

JAMA ophthalmology
IMPORTANCE: Until now, other than complex neurologic tests, there have been no readily accessible and reliable indicators of neurologic dysfunction among patients with Parkinson disease (PD). This study was conducted to determine the role of fundus p...