Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Machine Learning

Background:

• Convolutional Neural Networks (CNNs) excel at cell/nucleus quantification but require extensive annotated data.
• Acquiring annotated microscopy data is costly and time-consuming, hindering widespread application.
• Re-training or fine-tuning models on new datasets requires manual annotation, limiting throughput.

Purpose of the Study:

• To develop a novel adversarial domain adaptation method for cell/nucleus quantification across different microscopy image modalities.
• To reduce the reliance on large, annotated datasets for accurate cell detection.
• To enable high-throughput image analysis without extensive manual labeling.

Main Methods:

• A fully convolutional network detector was trained using task-specific cycle-consistent adversarial learning for pixel-level domain adaptation.
• The method employs adversarial learning to adapt source domain images to the target domain.
• Pseudo-labels were generated on target data using the adapted model, followed by fine-tuning for performance enhancement.

Main Results:

• The proposed method achieved significant improvements in cell/nucleus detection across multiple cross-modality datasets.
• Performance gains surpassed existing baseline methods and a state-of-the-art deep domain adaptation approach.
• The method demonstrated competitive performance compared to fully supervised models trained with complete target labels.

Conclusions:

• Adversarial domain adaptation offers a powerful solution for cell/nucleus quantification with limited annotated data.
• The developed method effectively bridges the domain gap in microscopy image analysis.
• This approach enhances the efficiency and applicability of deep learning models in biological image quantification.