Opportunities and challenges in explainable artificial intelligence (xai): A survey
Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …
healthcare, self-driving vehicles, and military which have direct impact on human lives …
[HTML][HTML] Explaining deep neural networks: A survey on the global interpretation methods
A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
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 …
Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology
The COVID-19 pandemic has resulted in massive disruptions within health care, both
directly as a result of the infectious disease outbreak, and indirectly because of public health …
directly as a result of the infectious disease outbreak, and indirectly because of public health …
Artificial intelligence for screening of multiple retinal and optic nerve diseases
Importance The lack of experienced ophthalmologists limits the early diagnosis of retinal
diseases. Artificial intelligence can be an efficient real-time way for screening retinal …
diseases. Artificial intelligence can be an efficient real-time way for screening retinal …
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs
F Li, Y Wang, T Xu, L Dong, L Yan, M Jiang, X Zhang… - Eye, 2022 - nature.com
Objectives To present and validate a deep ensemble algorithm to detect diabetic retinopathy
(DR) and diabetic macular oedema (DMO) using retinal fundus images. Methods A total of …
(DR) and diabetic macular oedema (DMO) using retinal fundus images. Methods A total of …
[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 …
Idrid: Diabetic retinopathy–segmentation and grading challenge
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss,
predominantly affecting the working-age population across the globe. Screening for DR …
predominantly affecting the working-age population across the globe. Screening for DR …
Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study
Background Medical artificial intelligence (AI) has entered the clinical implementation
phase, although real-world performance of deep-learning systems (DLSs) for screening …
phase, although real-world performance of deep-learning systems (DLSs) for screening …
Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
The exponential increase in the number of diabetics around the world has led to an equally
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …