AIMC Topic: Deep Learning

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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

NeuroImage
Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysi...

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Journal of applied clinical medical physics
PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological...

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

Stroke
BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about...

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing.

Proceedings of the National Academy of Sciences of the United States of America
Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical res...

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.

Scientific reports
The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to v...

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Physics in medicine and biology
. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dos...

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

Physics in medicine and biology
. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial...

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

PLoS computational biology
The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene driv...

Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

IEEE journal of translational engineering in health and medicine
UNLABELLED: - Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).

Deep Learning for Localized Detection of Optic Disc Hemorrhages.

American journal of ophthalmology
PURPOSE: To develop an automated deep learning system for detecting the presence and location of disc hemorrhages in optic disc photographs.