Factorized Convolution with Spectral Normalization for Fundus Screening

Ming Zeng, Na Zeng, Jiansheng Fang, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Convolutional neural network (CNN) models have been widely used for fundus-based disease screening, but the model deployment is challenging due to the large demand for computing resources. The low-rank decomposition is usually used to compress CNN models. However, the reduction of model parameters often leads to performance degradation. Therefore, we propose a factorized convolution with spectral normalization, named FConvSN, to reduce the model complexity while maintaining an ideal performance. FConvSN applies the spectral norm to constrain the weight in the direction of the spectral norm to achieve weight decay, thereby improving the generalizability. Since the features of fundus images appear as a highly skewed distribution, factorized convolution can be used to promote the sharing of convolution parameters, and spectral normalization can further prevent excessive weight in the spectral norm direction. We have conducted experiments on the fundus dataset to prove that our FConvSN can achieve performance comparable to standard convolution.

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
ISBN (Electronic)9781665429238
Publication statusPublished - 2022
Externally publishedYes
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Publication series

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


Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022


  • Factorized convolution
  • Fundus screening
  • Spectral normalization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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