[HTML][HTML] Deep learning in medical ultrasound analysis: a review
Ultrasound (US) has become one of the most commonly performed imaging modalities in
clinical practice. It is a rapidly evolving technology with certain advantages and with unique …
clinical practice. It is a rapidly evolving technology with certain advantages and with unique …
Artificial intelligence in musculoskeletal ultrasound imaging
Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging
that facilitates the rapid and dynamic assessment of musculoskeletal components …
that facilitates the rapid and dynamic assessment of musculoskeletal components …
Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment
Ultrasound imaging is a patient-friendly and robust technique for studying physiological and
pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound …
pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound …
Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review
Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging
tasks, such as disease diagnosis and image reconstruction. AI applications in …
tasks, such as disease diagnosis and image reconstruction. AI applications in …
Feature-guided CNN for denoising images from portable ultrasound devices
As a non-invasive medical imaging scanning device, ultrasound has greatly increased the
efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being …
efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being …
NHBS-Net: A feature fusion attention network for ultrasound neonatal hip bone segmentation
Ultrasound is a widely used technology for diagnosing developmental dysplasia of the hip
(DDH) because it does not use radiation. Due to its low cost and convenience, 2-D …
(DDH) because it does not use radiation. Due to its low cost and convenience, 2-D …
Diagnostic performance of a new convolutional neural network algorithm for detecting developmental dysplasia of the hip on anteroposterior radiographs
HS Park, K Jeon, YJ Cho, SW Kim… - Korean journal of …, 2020 - pmc.ncbi.nlm.nih.gov
Objective To evaluate the diagnostic performance of a deep learning algorithm for the
automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) …
automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) …
A novel bio-inspired deep learning approach for liver cancer diagnosis
RM Ghoniem - Information, 2020 - mdpi.com
Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional
feature engineering methods, which have several drawbacks including redundant features …
feature engineering methods, which have several drawbacks including redundant features …
Ultrasound bone segmentation: A sco** review of techniques and validation practices
Ultrasound bone segmentation is an important yet challenging task for many clinical
applications. Several works have emerged attempting to improve and automate bone …
applications. Several works have emerged attempting to improve and automate bone …
Accuracy of new deep learning model-based segmentation and key-point multi-detection method for ultrasonographic developmental dysplasia of the hip (DDH) …
Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia
of the hip (DDH) screening. However, the effectiveness of this technique is subject to …
of the hip (DDH) screening. However, the effectiveness of this technique is subject to …