Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy

• URL: http://arxiv.org/abs/2511.12006v1
• Date: Sat, 15 Nov 2025 03:01:05 GMT
• Title: Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy
• Authors: Kai-Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho-Jui Hsieh, Neil Y. C. Lin,
• Abstract summary: We introduce Subnetwork Image Translation ADDA with automatic depth selection (SIT-ADDA-Auto)<n>We show that adapting only the earliest convolutional layers, while freezing deeper layers, yields reliable transfer.<n>Our results provide a design rule for label-free adaptation in microscopy and a recipe for field settings; the code is publicly available.
• Score: 65.15943255667733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning is transforming microscopy, yet models often fail when applied to images from new instruments or acquisition settings. Conventional adversarial domain adaptation (ADDA) retrains entire networks, often disrupting learned semantic representations. Here, we overturn this paradigm by showing that adapting only the earliest convolutional layers, while freezing deeper layers, yields reliable transfer. Building on this principle, we introduce Subnetwork Image Translation ADDA with automatic depth selection (SIT-ADDA-Auto), a self-configuring framework that integrates shallow-layer adversarial alignment with predictive uncertainty to automatically select adaptation depth without target labels. We demonstrate robustness via multi-metric evaluation, blinded expert assessment, and uncertainty-depth ablations. Across exposure and illumination shifts, cross-instrument transfer, and multiple stains, SIT-ADDA improves reconstruction and downstream segmentation over full-encoder adaptation and non-adversarial baselines, with reduced drift of semantic features. Our results provide a design rule for label-free adaptation in microscopy and a recipe for field settings; the code is publicly available.

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