# Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability

arXiv: https://arxiv.org/abs/2411.19514 · HTML: https://mlsanity.com/paper/2411.19514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Progressive Feature Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1811.08585/) (2018)
- [Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules](https://mlsanity.com/paper/2106.10962/) (2021)
- [TE-YOLOF: Tiny and efficient YOLOF for blood cell detection](https://mlsanity.com/paper/2108.12313/) (2021)

## Built on by (most cited first, 1 of 1)

- [Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy](https://mlsanity.com/paper/2511.12006/) (2025)

## Abstract (arXiv)

Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges by i) enhancing the generalizability of AI-enabled microscopy for bacterial classification using adversarial domain adaptation and ii) comparing the performance of single-target and multi-domain adaptation. Three Gram-positive (Bacillus coagulans, Bacillus subtilis, Listeria innocua) and three Gram-negative (E. coli, Salmonella Enteritidis, Salmonella Typhimurium) strains were classified. EfficientNetV2 served as the backbone architecture, leveraging fine-grained feature extraction for small targets. Few-shot learning enabled scalability, with domain-adversarial neural networks (DANNs) addressing single domains and multi-DANNs (MDANNs) generalizing across all target domains. The model was trained on source domain data collected under controlled conditions (phase contrast microscopy, 60x magnification, 3-h bacterial incubation) and evaluated on target domains with variations in microscopy modality (brightfield, BF), magnification (20x), and extended incubation to compensate for lower resolution (20x-5h). DANNs improved target domain classification accuracy by up to 54.45% (20x), 43.44% (20x-5h), and 31.67% (BF), with minimal source domain degradation (<4.44%). MDANNs achieved superior performance in the BF domain and substantial gains in the 20x domain. Grad-CAM and t-SNE visualizations validated the model's ability to learn domain-invariant features across diverse conditions. This study presents a scalable and adaptable framework for bacterial classification, reducing reliance on extensive sample preparation and enabling application in decentralized and resource-limited environments.