AIMC Topic: Deep Learning

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Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.

Journal of applied clinical medical physics
PURPOSE: Deep learning-based virtual patient-specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefor...

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation.

Computers in biology and medicine
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of ...

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images.

Medical image analysis
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support set...

Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.

Waste management (New York, N.Y.)
Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of...

A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.

Annals of neurology
Increased intracranial pressure (ICP) causes disability and mortality in the neurointensive care population. Current methods for monitoring ICP are invasive. We designed a deep learning framework using a domain adversarial neural network to estimate ...

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification acc...

Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children.

JAMA network open
IMPORTANCE: Abusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs ...

Obstacle Detection System for Navigation Assistance of Visually Impaired People Based on Deep Learning Techniques.

Sensors (Basel, Switzerland)
Visually impaired people seek social integration, yet their mobility is restricted. They need a personal navigation system that can provide privacy and increase their confidence for better life quality. In this paper, based on deep learning and neura...

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via d...