AIMC Topic: Deep Learning

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Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties.

NMR in biomedicine
The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metri...

Urban Heat Island Intensity Changes in Guangdong-Hong Kong-Macao Greater Bay Area of China Revealed by Downscaling MODIS LST with Deep Learning.

International journal of environmental research and public health
The urban heat island (UHI) effect caused by urbanization negatively impacts the ecological environment and human health. It is crucial for urban planning and social development to monitor the urban heat island effect and study its mechanism. Due to ...

Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns.

The Journal of arthroplasty
BACKGROUND: In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep le...

Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction.

Medical physics
BACKGROUND: The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefr...

A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler.

Sensors (Basel, Switzerland)
The need to overcome the challenges of visual inspections conducted by domain experts drives the recent surge in visual inspection research. Typical manual industrial data analysis and inspection for defects conducted by trained personnel are expensi...

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

Scientific reports
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss t...

Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.

Biomedical engineering online
BACKGROUND: The evaluation of refraction is indispensable in ophthalmic clinics, generally requiring a refractor or retinoscopy under cycloplegia. Retinal fundus photographs (RFPs) supply a wealth of information related to the human eye and might pro...

RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study.

BMC medical imaging
BACKGROUND: It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains dif...

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

European radiology
OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algori...

Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.

Cancer cytopathology
BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-...