Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review
G Quer, R Arnaout, M Henne, R Arnaout - Journal of the American College …, 2021 - jacc.org
The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …
diagnostic patterns that guide treatment and follow response. Today, increasingly …
A review of deep learning on medical image analysis
Compared with common deep learning methods (eg, convolutional neural networks),
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …
recognition is a challenging problem for radiologists in health monitoring and automated …
Transfer learning for medical images analyses: A survey
The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …
science and also revitalized numerous fields where traditional machine learning methods …
Artificial intelligence, machine (deep) learning and radio (geno) mics: definitions and nuclear medicine imaging applications
D Visvikis, C Cheze Le Rest, V Jaouen… - European journal of …, 2019 - Springer
Techniques from the field of artificial intelligence, and more specifically machine (deep)
learning methods, have been core components of most recent developments in the field of …
learning methods, have been core components of most recent developments in the field of …
A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning
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 …
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network
MI Sharif, JP Li, J Amin, A Sharif - Complex & Intelligent Systems, 2021 - Springer
Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The
abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to …
abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to …
Transfer learning approaches for neuroimaging analysis: a scoping review
Deep learning algorithms have been moderately successful in diagnoses of diseases by
analyzing medical images especially through neuroimaging that is rich in annotated data …
analyzing medical images especially through neuroimaging that is rich in annotated data …
Fine-tuning U-Net for ultrasound image segmentation: different layers, different outcomes
One way of resolving the problem of scarce and expensive data in deep learning for medical
applications is using transfer learning and fine-tuning a network which has been trained on …
applications is using transfer learning and fine-tuning a network which has been trained on …