QuickQual: Lightweight, convenient retinal image quality scoring with
  off-the-shelf pretrained models
Justin Engelmann Amos Storkey Miguel O. Bernabeu
Image quality remains a key problem for both traditional and deep learning
(DL)-based approaches to retinal image analysis, but identifying poor quality
images can be time consuming and subjective. Thus, automated methods for
retinal image quality scoring (RIQS) are needed. The current state-of-the-art
is MCFNet, composed of three Densenet121 backbones each operating in a
different colour space. MCFNet, and the EyeQ dataset released by the same
authors, was a huge step forward for RIQS. We present QuickQual, a simple
approach to RIQS, consisting of a single off-the-shelf ImageNet-pretrained
Densenet121 backbone plus a Support Vector Machine (SVM). QuickQual performs
very 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 with
generic perceptual features learned on natural images, as opposed to requiring
DL models trained on large amounts of fundus images. Additionally, we propose a
Fixed Prior linearisation scheme, that converts EyeQ from a 3-way
classification to a continuous logistic regression task. For this task, we
present a second model, QuickQual MEga Minified Estimator (QuickQual-MEME),
that consists of only 10 parameters on top of an off-the-shelf Densenet121 and
can distinguish between gradable and ungradable images with an accuracy of
89.18% (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) is
already contained in this paper.