Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography

TL;DR: The primary goal is to collaborate with experts to develop computer-aided diagnosis systems and test them in various hospital settings with varying picture quality and highlight the remaining gaps and future research avenues to pursue.

TL;DR: This systematic review of machine learning and deep learning techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on, helps readers and researchers understand critical aspects of AI related to glaucoma.

TL;DR: This article focuses on the use of convolutional neural networks to classify background images of DR according to disease severity and on the application of pooling, Softmax activation to achieve greater accuracy.

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.

TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.

TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.

TL;DR: Glaucoma is the second leading cause of blindness worldwide, disproportionately affecting women and Asians, and it will be 60.5 million people with OAG and ACG in 2010, increasing to 79.6 million by 2020, and of these, 74% will have OAG.