AIMC Topic: Deep Learning

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[Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).

Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils.

The Tohoku journal of experimental medicine
Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infecte...

Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.

Translational vision science & technology
PURPOSE: To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT).

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

Translational vision science & technology
PURPOSE: To compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpR...

Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images.

Translational vision science & technology
PURPOSE: This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images.

Convolutional Neural Network Models for Automatic Preoperative Severity Assessment in Unilateral Cleft Lip.

Plastic and reconstructive surgery
BACKGROUND: Despite the wide range of cleft lip morphology, consistent scales to categorize preoperative severity do not exist. Machine learning has been used to increase accuracy and efficiency in detection and rating of multiple conditions, yet it ...

An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study.

The Lancet. Digital health
BACKGROUND: Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging-Reporting & Data System (PI-RADS)-b...

Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection.

The British journal of radiology
OBJECTIVE: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and...