Traumatic brain injury: progress and challenges in prevention, clinical care, and research
Executive summary Traumatic brain injury (TBI) has the highest incidence of all common
neurological disorders, and poses a substantial public health burden. TBI is increasingly …
neurological disorders, and poses a substantial public health burden. TBI is increasingly …
External validation of deep learning algorithms for radiologic diagnosis: a systematic review
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
Curriculum learning: A survey
Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …
ones, using curriculum learning can provide performance improvements over the standard …
Medical imaging and nuclear medicine: a Lancet Oncology Commission
The diagnosis and treatment of patients with cancer requires access to imaging to ensure
accurate management decisions and optimal outcomes. Our global assessment of imaging …
accurate management decisions and optimal outcomes. Our global assessment of imaging …
Artificial intelligence and acute stroke imaging
JE Soun, DS Chow, M Nagamine… - American Journal …, 2021 - Am Soc Neuroradiology
Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge
AE Flanders, LM Prevedello, G Shih… - Radiology: Artificial …, 2020 - pubs.rsna.org
Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT
Hemorrhage Challenge | Radiology: Artificial Intelligence RSNA "skipMainNavigation" …
Hemorrhage Challenge | Radiology: Artificial Intelligence RSNA "skipMainNavigation" …
[HTML][HTML] A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans
Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use
Background Machine learning (ML), a type of artificial intelligence (AI) technology that uses
a data-driven approach for pattern recognition, has been shown to be beneficial for many …
a data-driven approach for pattern recognition, has been shown to be beneficial for many …
Contrastive self-supervised learning from 100 million medical images with optional supervision
Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However …
assessment requires the creation of large sets of annotated training examples. However …
Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …