AIMC Topic: Deep Learning

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Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.

BioMed research international
Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncon...

Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: Focus on image quality and reduced scan time.

European journal of radiology
PURPOSE: This study aimed to evaluate the feasibility of accelerated DLR (deep learning reconstruction) single-shot echo planar imaging (ss-EPI) for diffusion-weighted image (DWI) in patients with breast cancers in comparison to conventional ss-EPI.

The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.

Zeitschrift fur medizinische Physik
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an e...

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.

European radiology
OBJECTIVES: Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was t...

A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning.

Clinical imaging
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments ...

Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence.

Environmental science and pollution research international
This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two...

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future.

Photodiagnosis and photodynamic therapy
Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. Accor...

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strateg...

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning.

Sensors (Basel, Switzerland)
There are physical Human-Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a...

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach.

Sensors (Basel, Switzerland)
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To ...