Multimodal image encoding pre-training for diabetic retinopathy grading

TL;DR: Experimental results on retinal image quality dataset Eye-Quality demonstrate the effectiveness of the proposed GuidedNet, where the dark and bright channel priors are embedded into the start layer of network to improve the discriminate ability of deep features.

TL;DR: This paper proposed the three models based on a deep learning approach for recognizing blood vessels from retinal images using region-based segmentation techniques and it is observed that more thin blood vessels are segmented on the retinal image in the HRF dataset using model-3.

TL;DR: This review summarizes the advancements and prospects of multi-modal ophthalmic AI based on deep learning, highlighting its diagnostic efficacy in various ophthalmic diseases, but also acknowledging challenges in its clinical application and future research directions.

TL;DR: In this paper , an artificial intelligence-assisted automated diabetic retinopathy (ADRC) system is proposed, which is an excellent way to reduce the pressure of incorrect diagnoses as a result.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.

TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.