Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability

Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability

AI-enabled microscopy is emerging for rapid bacterial classification, yet its utility remains limited in dynamic or resource-limited settings due to imaging variability. This study aims to enhance the generalizability of AI microscopy using domain adaptation techniques. Six bacterial species, includ...

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Bibliographic Details
Main Authors: Siddhartha Bhattacharya, Aarham Wasit, J Mason Earles, Nitin Nitin, Jiyoon Yi
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Artificial Intelligence
Subjects:
AI microscopy
bacterial classification
domain adaptation
deep learning
foodborne bacteria
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1632344/full
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https://www.frontiersin.org/articles/10.3389/frai.2025.1632344/full

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