AIMC Topic: Deep Learning

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Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.

Medical physics
OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative ass...

Deep learning classification of lung cancer histology using CT images.

Scientific reports
Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical imag...

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

Physics in medicine and biology
We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator seg...

Application of deep learning in automatic detection of technical and tactical indicators of table tennis.

PloS one
A DCNN-LSTM (Deep Convolutional Neural Network-Long Short Term Memory) model is proposed to recognize and track table tennis's real-time trajectory in complex environments, aiming to help the audiences understand competition details and provide a ref...

Batch Coherence-Driven Network for Part-Aware Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Existing part-aware person re-identification methods typically employ two separate steps: namely, body part detection and part-level feature extraction. However, part detection introduces an additional computational cost and is inherently challenging...

A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images.

Orthodontics & craniofacial research
OBJECTIVES: This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.

Journal of cardiovascular pharmacology and therapeutics
BACKGROUND: Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may pr...

DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is deve...

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

Journal of digital imaging
The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these ey...

Deep learning-based point-scanning super-resolution imaging.

Nature methods
Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation and signal-to-noise ratio (SNR) of point-...