AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
[HTML][HTML] Deep learning for chest X-ray analysis: A survey
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …
death cases and affected all sectors of human life. With gradual progression of time, COVID …
Criteria for the translation of radiomics into clinically useful tests
EP Huang, JPB O'Connor, LM McShane… - Nature reviews Clinical …, 2023 - nature.com
Computer-extracted tumour characteristics have been incorporated into medical imaging
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …
Deep learning in medical image registration: a review
This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …
methods. We summarized the latest developments and applications of DL-based registration …
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Labeled medical imaging data is scarce and expensive to generate. To achieve
generalizable deep learning models large amounts of data are needed. Standard data …
generalizable deep learning models large amounts of data are needed. Standard data …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
The 'Digital Twin'to enable the vision of precision cardiology
J Corral-Acero, F Margara, M Marciniak… - European heart …, 2020 - academic.oup.com
Providing therapies tailored to each patient is the vision of precision medicine, enabled by
the increasing ability to capture extensive data about individual patients. In this position …
the increasing ability to capture extensive data about individual patients. In this position …