Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images
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 prototypi...
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2023
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ftsrucpubl:oai:pure.atira.dk:publications/fe02008d-3d9b-4251-ba06-637538557814 2024-06-09T07:44:44+00:00 Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images Carmichael, AFBC Baily, Johanna Reeves, A Ochoa, Gabriela Boerlage, AS Turnbull, Jimmy Gunn, GJ Allshire, Rosa Bhowmik, Deepayan 2023-10-25 application/pdf https://pure.sruc.ac.uk/en/publications/fe02008d-3d9b-4251-ba06-637538557814 https://pure.sruc.ac.uk/ws/files/86228213/Newest_update_-_GILL_HEALTH_INITIATIVE_2023_23.10_185194.pdf eng eng https://pure.sruc.ac.uk/en/publications/fe02008d-3d9b-4251-ba06-637538557814 info:eu-repo/semantics/openAccess Carmichael , AFBC , Baily , J , Reeves , A , Ochoa , G , Boerlage , AS , Turnbull , J , Gunn , GJ , Allshire , R & Bhowmik , D 2023 , ' Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images ' , Gill Health Initiative 2023 , Oslo , Norway , 25/10/23 - 26/10/23 . conferenceObject 2023 ftsrucpubl 2024-05-16T14:34:53Z 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. Conference Object Atlantic salmon SRUC (Scotland's Rural College): Research Portal |
institution |
Open Polar |
collection |
SRUC (Scotland's Rural College): Research Portal |
op_collection_id |
ftsrucpubl |
language |
English |
description |
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. |
format |
Conference Object |
author |
Carmichael, AFBC Baily, Johanna Reeves, A Ochoa, Gabriela Boerlage, AS Turnbull, Jimmy Gunn, GJ Allshire, Rosa Bhowmik, Deepayan |
spellingShingle |
Carmichael, AFBC Baily, Johanna Reeves, A Ochoa, Gabriela Boerlage, AS Turnbull, Jimmy Gunn, GJ Allshire, Rosa Bhowmik, Deepayan Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
author_facet |
Carmichael, AFBC Baily, Johanna Reeves, A Ochoa, Gabriela Boerlage, AS Turnbull, Jimmy Gunn, GJ Allshire, Rosa Bhowmik, Deepayan |
author_sort |
Carmichael, AFBC |
title |
Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
title_short |
Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
title_full |
Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
title_fullStr |
Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
title_full_unstemmed |
Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images |
title_sort |
artificial intelligence for computer-assisted diagnosis of hyperplasia in atlantic salmon gill histology images |
publishDate |
2023 |
url |
https://pure.sruc.ac.uk/en/publications/fe02008d-3d9b-4251-ba06-637538557814 https://pure.sruc.ac.uk/ws/files/86228213/Newest_update_-_GILL_HEALTH_INITIATIVE_2023_23.10_185194.pdf |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_source |
Carmichael , AFBC , Baily , J , Reeves , A , Ochoa , G , Boerlage , AS , Turnbull , J , Gunn , GJ , Allshire , R & Bhowmik , D 2023 , ' Artificial Intelligence for Computer-Assisted Diagnosis of Hyperplasia in Atlantic Salmon Gill Histology Images ' , Gill Health Initiative 2023 , Oslo , Norway , 25/10/23 - 26/10/23 . |
op_relation |
https://pure.sruc.ac.uk/en/publications/fe02008d-3d9b-4251-ba06-637538557814 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1801373546334650368 |