Structural visual guidance attention networks in retinopathy of prematurity

V. Yildiz, S. Ioannidis, I. Yildiz, P. Tian, J. P. Campbell, S. Ostmo, J. Kalpathy-Cramer, M. F. Chiang, D. Erdogmus, J. Dy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Convolutional neural networks (CNNs) have shown great performance in medical diagnostic applications. However, because their black-box nature, clinicians are reluctant to trust CNN diagnostic outcomes. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Clinicians can often provide domain knowledge on relevant features: e.g., to diagnose retinopathy of prematurity (ROP), structural information such as tortuosity of vessels aid clinicians in diagnosing ROP. We propose a Structural Visual Guidance Attention Networks (SVGA-Net) method, that leverages structural domain knowledge to guide visual attention in CNNs. Experiments on a dataset of 5512 posterior retinal images, taken using a wide-angle fundus camera, show that SVGA-Net achieves 0.987 and 0.979 AUC to predict plus and normal categories, respectively. SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance, baseline CNNs, and CNNs with structured masks.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665412469
StatePublished - Apr 13 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021


  • Attention
  • CNN
  • Interpretability
  • ROP

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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