Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
The simultaneous maturation of multiple digital and telecommunications technologies in
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
LP Cen, J Ji, JW Lin, ST Ju, HJ Lin, TP Li… - Nature …, 2021 - nature.com
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses
and appropriate treatments. Single disease-based deep learning algorithms had been …
and appropriate treatments. Single disease-based deep learning algorithms had been …
Applications of deep learning in fundus images: A review
The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …
importance. Due to its powerful performance, deep learning is becoming more and more …
A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
Health data that are publicly available are valuable resources for digital health research.
Several public datasets containing ophthalmological imaging have been frequently used in …
Several public datasets containing ophthalmological imaging have been frequently used in …
Explaining in style: Training a gan to explain a classifier in stylespace
Image classification models can depend on multiple different semantic attributes of the
image. An explanation of the decision of the classifier needs to both discover and visualize …
image. An explanation of the decision of the classifier needs to both discover and visualize …
Code-free deep learning for multi-modality medical image classification
A number of large technology companies have created code-free cloud-based platforms that
allow researchers and clinicians without coding experience to create deep learning …
allow researchers and clinicians without coding experience to create deep learning …
[HTML][HTML] Predicting the risk of developing diabetic retinopathy using deep learning
Background Diabetic retinopathy screening is instrumental to preventing blindness, but
scaling up screening is challenging because of the increasing number of patients with all …
scaling up screening is challenging because of the increasing number of patients with all …
Balanced-mixup for highly imbalanced medical image classification
A Galdran, G Carneiro… - Medical Image Computing …, 2021 - Springer
Highly imbalanced datasets are ubiquitous in medical image classification problems. In such
problems, it is often the case that rare classes associated to less prevalent diseases are …
problems, it is often the case that rare classes associated to less prevalent diseases are …
[HTML][HTML] Deep learning for diabetic retinopathy detection and classification based on fundus images: A review
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading
cause of blindness globally. Early detection and treatment are necessary in order to delay or …
cause of blindness globally. Early detection and treatment are necessary in order to delay or …