English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Image quality remains a key problem for both traditional and deep learning(DL)-based approaches to retinal image analysis, but identifying poor qualityimages can be time consuming and subjective. Thus, automated methods forretinal image quality scoring (RIQS) are needed. The current state-of-the-artis MCFNet, composed of three Densenet121 backbones each operating in adifferent colour space. MCFNet, and the EyeQ dataset released by the sameauthors, was a huge step forward for RIQS. We present QuickQual, a simpleapproach to RIQS, consisting of a single off-the-shelf ImageNet-pretrainedDensenet121 backbone plus a Support Vector Machine (SVM). QuickQual performsvery well, setting a new state-of-the-art for EyeQ (Accuracy: 88.50% vs 88.00%for MCFNet; AUC: 0.9687 vs 0.9588). This suggests that RIQS can be solved withgeneric perceptual features learned on natural images, as opposed to requiringDL models trained on large amounts of fundus images. Additionally, we propose aFixed Prior linearisation scheme, that converts EyeQ from a 3-wayclassification to a continuous logistic regression task. For this task, wepresent a second model, QuickQual MEga Minified Estimator (QuickQual-MEME),that consists of only 10 parameters on top of an off-the-shelf Densenet121 andcan distinguish between gradable and ungradable images with an accuracy of89.18% (AUC: 0.9537). Code and model are available on GitHub:https://github.com/justinengelmann/QuickQual . QuickQual is so lightweight,that the entire inference code (and even the parameters for QuickQual-MEME) isalready contained in this paper.

QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models