Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).
Med Phys
; 50(1): 449-464, 2023 Jan.
Article
in En
| MEDLINE
| ID: mdl-36184848
ABSTRACT
OBJECTIVE:
To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification.METHODS:
The neural understanding network (NUN) is formed by two components (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models.RESULTS:
The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models.CONCLUSION:
The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Retinal Vein Occlusion
/
Nuns
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Med Phys
Year:
2023
Document type:
Article
Affiliation country:
United States
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA