Analysing hyperplasia in Atlantic salmon gills using empirical wavelets

Alexander F.B. Carmichael*, Johanna L. Baily, Aaron Reeves, Gabriela Ochoa, Annette S. Boerlage, George Gunn, Rosa Allshire, Deepayan Bhowmik

*Corresponding author for this work

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


Measuring hyperplasia in Atlantic salmon gills can give important insight into fish health and environmental conditions such as water quality. This paper proposes a novel histology image classification technique to identify hyperplastic regions using an emerging signal decomposition technique, Empirical Wavelet Transform (EWT) in combination with a fully connected neural network (FCNN). Due to its adaptive nature, we hypothesise and show that EWT effectively represents unique features of gill histopathology whole slide images that help in the classification task. Our hybrid approach is unique and significantly outperformed regular deep learning-based methods considering a joint speed-accuracy metric.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationDigital and Computational Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510660472
Publication statusFirst published - 6 Apr 2023
EventMedical Imaging 2023: Digital and Computational Pathology - San Diego, United States
Duration: 19 Feb 202323 Feb 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Digital and Computational Pathology
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Publisher Copyright:
© 2023 SPIE.


  • Atlantic salmon gills
  • deep learning
  • digital pathology
  • empirical wavelet transform
  • histopathology
  • Hyperplasia
  • image classification


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