[HTML][HTML] Trustworthy AI: closing the gap between development and integration of AI systems in ophthalmic practice
C González-Gonzalo, EF Thee, CCW Klaver… - Progress in retinal and …, 2022 - Elsevier
An increasing number of artificial intelligence (AI) systems are being proposed in
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
A holistic overview of deep learning approach in medical imaging
Medical images are a rich source of invaluable necessary information used by clinicians.
Recent technologies have introduced many advancements for exploiting the most of this …
Recent technologies have introduced many advancements for exploiting the most of this …
[HTML][HTML] ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images
In medical imaging, segmentation plays a vital role towards the interpretation of X-ray
images where salient features are extracted with the help of image segmentation. Without …
images where salient features are extracted with the help of image segmentation. Without …
Directional connectivity-based segmentation of medical images
Anatomical consistency in biomarker segmentation is crucial for many medical image
analysis tasks. A promising paradigm for achieving anatomically consistent segmentation …
analysis tasks. A promising paradigm for achieving anatomically consistent segmentation …
Global and local feature reconstruction for medical image segmentation
Learning how to capture long-range dependencies and restore spatial information of down-
sampled feature maps are the basis of the encoder-decoder structure networks in medical …
sampled feature maps are the basis of the encoder-decoder structure networks in medical …
[HTML][HTML] Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning
Purpose To apply a deep learning algorithm for automated, objective, and comprehensive
quantification of optical coherence tomography (OCT) scans to a large real-world dataset of …
quantification of optical coherence tomography (OCT) scans to a large real-world dataset of …
Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
Background In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate
estimation of multi-class retinal fluid (MRF) is required for the activity prescription and …
estimation of multi-class retinal fluid (MRF) is required for the activity prescription and …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
FedMix: Mixed supervised federated learning for medical image segmentation
The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …
learning model without sharing data. However, the existing methods for training an image …
[HTML][HTML] AI-based monitoring of retinal fluid in disease activity and under therapy
U Schmidt-Erfurth, GS Reiter, S Riedl… - Progress in retinal and …, 2022 - Elsevier
Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by
high-resolution three-dimensional optical coherence tomography (OCT), which is used …
high-resolution three-dimensional optical coherence tomography (OCT), which is used …