Artificial Intelligence can assist diagnosis of hyperplasia in Atlantic Salmon Gill Histology Images

One of the key histological indicators of declining Atlantic salmon gill health is hyperplasia, a condition involving abnormal cell growth. To develop computer assistance to conventional human diagnosis and quantification of hyperplasia, we've pioneered an innovative AI methodology for categorizing histology images by focusing on regions displaying hyperplasia. Our strategy entails evaluating image textures through groundbreaking signal processing techniques, in tandem with deep learning approaches. We demonstrate that our technique adeptly captures hyperplasia in whole-slide images, thereby providing a quantitative classification process. In contrast to other more conventional deep learning methodologies, our strategy showcases unparalleled performance in both speed and performance. As we advance this approach, it holds the potential to provide a quantitative computer-assisted hyperplasia score to support histopathological diagnosis by humans. The outlined procedure can be adapted to evaluate other gill conditions and histopathological images beyond gills.