[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
[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 …
Underspecification presents challenges for credibility in modern machine learning
Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study
P Ruamviboonsuk, R Tiwari, R Sayres… - The Lancet Digital …, 2022 - thelancet.com
Background Diabetic retinopathy is a leading cause of preventable blindness, especially in
low-income and middle-income countries (LMICs). Deep-learning systems have the …
low-income and middle-income countries (LMICs). Deep-learning systems have the …
Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods
M Canayaz - Applied Soft Computing, 2022 - Elsevier
Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It
shows that an automatic image evaluation system is needed in the diagnosis of this disease …
shows that an automatic image evaluation system is needed in the diagnosis of this disease …
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 …
Automated diabetic retinopathy detection using horizontal and vertical patch division-based pre-trained DenseNET with digital fundus images
Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive
vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt …
vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt …
Active label cleaning for improved dataset quality under resource constraints
Imperfections in data annotation, known as label noise, are detrimental to the training of
machine learning models and have a confounding effect on the assessment of model …
machine learning models and have a confounding effect on the assessment of model …
Nuclear medicine and artificial intelligence: best practices for algorithm development
The nuclear medicine field has seen a rapid expansion of academic and commercial interest
in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of …
in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of …
Lesion-attention pyramid network for diabetic retinopathy grading
As one of the most common diabetic complications, diabetic retinopathy (DR) can cause
retinal damage, vision loss and even blindness. Automated DR grading technology has …
retinal damage, vision loss and even blindness. Automated DR grading technology has …