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\begin{document}

\title{Detecting Glaucoma from Fundus Photographs Using Deep Learning without
Convolutions: Transformer for Improved Generalization}



\author[1]{Rui Fan }%
\author[1]{Kamran Alipour }%
\author[1]{Christopher Bowd }%
\author[1]{Mark Christopher }%
\author[1]{Nicole Brye }%
\author[1]{James A. Proudfoot }%
\author[1]{Michael H. Goldbaum }%
\author[1]{Akram Belghith }%
\author[1]{Christopher A. Girkin }%
\author[1]{Massimo Fazio }%
\author[1]{Jeffrey M. Liebmann }%
\author[1]{Robert N. Weinreb }%
\author[1]{Michael Pazzani }%
\author[1]{David Kriegman }%
\author[1]{Linda M. Zangwill }%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
\textbf{Purpose:} To compare the diagnostic accuracy and explainability
of a new Vision Transformer deep learning technique, Data-efficient
image Transformer (DeiT), and Resnet-50, trained on fundus photographs
from the Ocular Hypertension Treatment Study (OHTS) to detect primary
open-angle glaucoma (POAG) and to identify the salient areas of the
photographs most important for each model's decision-making process.

\textbf{Study Design:} Evaluation of a diagnostic technology

\textbf{Subjects, Participants, and/or Controls:} 66,715 photographs
from 1,636 OHTS participants and an additional five external datasets of
16137 photographs of healthy and glaucoma eyes.

\textbf{Methods, Intervention, or Testing:} DeiT models were trained to
detect five ground truth OHTS POAG classifications: OHTS Endpoint
Committee POAG determinations due to disc changes (Model 1), visual
field changes (Model 2), or either disc or visual field changes (Model
3) and reading center determinations based on disc (Model 4) and visual
fields (Model 5). The best-performing DeiT models were compared to
ResNet-50 on OHTS and five external datasets.

\textbf{Main Outcome Measures:} Diagnostic performance was compared
using areas under the receiver operating characteristic curve (AUROC)
and sensitivities at fixed specificities. The explainability of the DeiT
and ResNet-50 models was compared by evaluating the attention maps
derived directly from DeiT to 3 gradient-weighted class activation map
generation strategies.

\textbf{Results:} Compared to our best-performing ResNet-50 models, the
DeiT models demonstrated similar performance on the OHTS test sets for
all five-ground truth POAG labels; AUROC ranged from 0.82 (Model 5) to
0.91 (Model 1). However, the AUROC of DeiT was consistently higher than
ResNet-50 on the five external datasets. For example, AUROC for the main
OHTS endpoint (Model 3) was between 0.08 and 0.20 higher in the DeiT
compared to ResNet-50 models. The saliency maps from the DeiT highlight
localized areas of the neuroretinal rim, suggesting the use of important
clinical features for classification, while the same maps in the
ResNet-50 models show a more diffuse, generalized distribution around
the optic disc,

\textbf{Conclusions:} Vision transformer has the potential to improve
the generalizability and explainability of deep learning models for the
detection of eye disease and possibly other medical conditions that rely
on imaging modalities for clinical diagnosis and management.%
\end{abstract}%



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