Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …
collides with machine learning applications in the field of medical imaging analysis and …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
[HTML][HTML] End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images
The automated analysis of eye fundus images is crucial towards facilitating the screening
and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the …
and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the …
[HTML][HTML] Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images
Background and objectives The study of the retinal vasculature represents a fundamental
stage in the screening and diagnosis of many high-incidence diseases, both systemic and …
stage in the screening and diagnosis of many high-incidence diseases, both systemic and …
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Deep learning has become a valuable tool for the automation of certain medical image
segmentation tasks, significantly relieving the workload of medical specialists. Some of …
segmentation tasks, significantly relieving the workload of medical specialists. Some of …
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and …
X Huang, Y Guo, J Huang, Z Li, T Zhang, K Cai… - arXiv preprint arXiv …, 2023 - arxiv.org
The spatial and quantitative parameters of macular holes are vital for diagnosis, surgical
choices, and post-op monitoring. Macular hole diagnosis and treatment rely heavily on …
choices, and post-op monitoring. Macular hole diagnosis and treatment rely heavily on …
The quest for early detection of retinal disease: 3D CycleGAN-based translation of optical coherence tomography into confocal microscopy
Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal
imaging, offering distinct advantages and limitations. In vivo OCT offers rapid, noninvasive …
imaging, offering distinct advantages and limitations. In vivo OCT offers rapid, noninvasive …
[HTML][HTML] Context encoder transfer learning approaches for retinal image analysis
During the last years, deep learning techniques have emerged as powerful alternatives to
solve biomedical image analysis problems. However, the training of deep neural networks …
solve biomedical image analysis problems. However, the training of deep neural networks …
RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification
The caliber and configuration of retinal blood vessels serve as important biomarkers for
various diseases and medical conditions. A thorough analysis of the retinal vasculature …
various diseases and medical conditions. A thorough analysis of the retinal vasculature …
[PDF][PDF] Machine Learning Models for Multimodal Retinal Imaging
XIN TIAN - 2024 - research-information.bris.ac.uk
Retinal imaging leverages various modalities, each with unique capabilities and limitations.
This thesis introduces innovative machine-learning frameworks designed to optimise the …
This thesis introduces innovative machine-learning frameworks designed to optimise the …