AIMC Topic: Deep Learning

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Multi-modal body part segmentation of infants using deep learning.

Biomedical engineering online
BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis. Thermography may be a non-contact and wireless alternative to ...

Tile-based microscopic image processing for malaria screening using a deep learning approach.

BMC medical imaging
BACKGROUND: Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enor...

Dense reinforcement learning for safety validation of autonomous vehicles.

Nature
One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critic...

Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study.

Frontiers in endocrinology
OBJECTIVE: To develop and validate an artificial intelligence diagnostic system based on X-ray imaging data for diagnosing vertebral compression fractures (VCFs).

Epoch and accuracy based empirical study for cardiac MRI segmentation using deep learning technique.

PeerJ
Cardiac magnetic resonance imaging (CMRI) is a non-invasive imaging technique to analyse the structure and function of the heart. It was enhanced considerably over several years to deliver functional information for diagnosing and managing cardiovasc...

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

Journal of digital imaging
The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventi...

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.

Skeletal radiology
OBJECTIVE: To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based r...

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery.

European urology
Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual three-dimensional (3D) models. Apart from correct model alignment and deformation, not all instrume...

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

Bone
BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone...