Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
Generative adversarial networks in medical image segmentation: A review
S Xun, D Li, H Zhu, M Chen, J Wang, J Li… - Computers in biology …, 2022 - Elsevier
Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …
of deep learning in 2014, it has received extensive attention from academia and industry …
Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical
diagnosis. The training of new sonographers and deep learning based algorithms for US …
diagnosis. The training of new sonographers and deep learning based algorithms for US …
SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation
Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
mustGAN: multi-stream generative adversarial networks for MR image synthesis
Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
[Retracted] Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that
can be captured by imaging modalities and translated into imaging features. Observing …
can be captured by imaging modalities and translated into imaging features. Observing …
Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning
F Liu, B Guan, Z Zhou, A Samsonov… - Radiology: Artificial …, 2019 - pubs.rsna.org
Purpose To investigate the feasibility of using a deep learning–based approach to detect an
anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the …
anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the …
MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping
Purpose To develop and evaluate a novel deep learning‐based image reconstruction
approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space …
approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space …
Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks
Purpose Fully automatic tissue segmentation is an essential step to translate quantitative
MRI techniques to clinical setting. The goal of this study was to develop a novel approach …
MRI techniques to clinical setting. The goal of this study was to develop a novel approach …
Improving robustness of deep learning based knee mri segmentation: Mixup and adversarial domain adaptation
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA)
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …