Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images

AFBC Carmichael*, Johanna Baily, A Reeves, Gabriela Ochoa, AS Boerlage, Jimmy Turnbull, GJ Gunn, Rosa Allshire, Deepayan Bhowmik

*Corresponding author for this work

Research output: Contribution to conferenceAbstract

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Abstract

Measuring hyperplasia in Atlantic salmon gills can provide valuable insights into fish health. In this study, we propose an innovative technique for classifying histology images to identify regions of hyperplasia. Our pipeline utilises novel signal processing techniques in conjunction with prototypical deep learning methods to analyse image texture. We hypothesise and demonstrate that our method effectively captures distinct features of gill histopathology whole-slide images, thereby enhancing the classification task. Compared to conventional deep learning methods, our hybrid approach exhibits exceptional performance in speed and accuracy. When further developed, the concept can support conventional histopathological assessment by providing a computer-assisted hyperplasia score as an objective quantitative histopathological endpoint. The workflow is translatable to other gill conditions and histopathology images beyond gills.
Original languageEnglish
Publication statusPrint publication - 25 Oct 2023
EventGill Health Initiative 2023 - Oslo, Norway
Duration: 25 Oct 202326 Oct 2023
https://www.gillhealthinitiative.org/

Conference

ConferenceGill Health Initiative 2023
Country/TerritoryNorway
CityOslo
Period25/10/2326/10/23
Internet address

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