AIMC Topic: Deep Learning

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Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.

Scientific reports
The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients ...

VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

Nature communications
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological ins...

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial.

Physics in medicine and biology
Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted...

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The c...

A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network.

Computational and mathematical methods in medicine
Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent a...

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.

Clinical imaging
PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT).

Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items.

Journal of the American College of Surgeons
BACKGROUND: Retained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence ...

Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO.

Journal of neuroscience methods
BACKGROUND: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicia...

Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the most frequent asymptomatic arrhythmias associated with significant morbidity and mortality. Identifying the susceptibility to AF based on routine or continuous ECG recording is of consi...

Automated Defect Detection From Ultrasonic Images Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nondestructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used nondestructive techniques is called ultrasonic inspection. The acquis...