Detection of multiple retinal diseases in ultra-widefield fundus images
using deep learning: data-driven identification of relevant regions
- URL: http://arxiv.org/abs/2203.06113v1
- Date: Fri, 11 Mar 2022 17:33:33 GMT
- Title: Detection of multiple retinal diseases in ultra-widefield fundus images
using deep learning: data-driven identification of relevant regions
- Authors: Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick, Emma Pead,
Amos Storkey, Miguel O. Bernabeu
- Abstract summary: Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view.
Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images.
We propose a DL model that can recognise multiple retinal diseases under more realistic conditions.
- Score: 2.20200533591633
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Ultra-widefield (UWF) imaging is a promising modality that captures a larger
retinal field of view compared to traditional fundus photography. Previous
studies showed that deep learning (DL) models are effective for detecting
retinal disease in UWF images, but primarily considered individual diseases
under less-than-realistic conditions (excluding images with other diseases,
artefacts, comorbidities, or borderline cases; and balancing healthy and
diseased images) and did not systematically investigate which regions of the
UWF images are relevant for disease detection. We first improve on the state of
the field by proposing a DL model that can recognise multiple retinal diseases
under more realistic conditions. We then use global explainability methods to
identify which regions of the UWF images the model generally attends to. Our
model performs very well, separating between healthy and diseased retinas with
an area under the curve (AUC) of 0.9206 on an internal test set, and an AUC of
0.9841 on a challenging, external test set. When diagnosing specific diseases,
the model attends to regions where we would expect those diseases to occur. We
further identify the posterior pole as the most important region in a purely
data-driven fashion. Surprisingly, 10% of the image around the posterior pole
is sufficient for achieving comparable performance to having the full images
available.
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