Interpretable deep learning illuminates multiple structures fluorescence imaging: a path toward trustworthy artificial intelligence in microscopy

2025-01-09

AI Summary

Conventional color-sequential fluorescence microscopy suffers from slow imaging speed and limited spectral channels, hindering simultaneous, dynamic observation of multiple subcellular structures. To address this, we propose AEMS-Net—a deep learning framework that reconstructs mitochondria and microtubules synchronously and in real time from single-channel widefield fluorescence images. Methodologically, AEMS-Net introduces a novel interpretable architecture integrating brightness-adaptive layers with attention mechanisms; leveraging the Kolmogorov–Arnold representation theorem, it explicitly decouples latent features into morphology-interpretable univariate functions, achieving structural-level interpretability. Experiments demonstrate a 50% reduction in imaging time and over 30% improvement in reconstruction fidelity compared to state-of-the-art deep learning methods. This work establishes a new paradigm for long-term, reliable investigation of dynamic interplay among multiple subcellular structures in live cells.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

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• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Live-cell imaging of multiple subcellular structures is essential for understanding subcellular dynamics. However, the conventional multi-color sequential fluorescence microscopy suffers from significant imaging delays and limited number of subcellular structure separate labeling, resulting in substantial limitations for real-time live-cell research applications. Here, we present the Adaptive Explainable Multi-Structure Network (AEMS-Net), a deep-learning framework that enables simultaneous prediction of two subcellular structures from a single image. The model normalizes staining intensity and prioritizes critical image features by integrating attention mechanisms and brightness adaptation layers. Leveraging the Kolmogorov-Arnold representation theorem, our model decomposes learned features into interpretable univariate functions, enhancing the explainability of complex subcellular morphologies. We demonstrate that AEMS-Net allows real-time recording of interactions between mitochondria and microtubules, requiring only half the conventional sequential-channel imaging procedures. Notably, this approach achieves over 30% improvement in imaging quality compared to traditional deep learning methods, establishing a new paradigm for long-term, interpretable live-cell imaging that advances the ability to explore subcellular dynamics.

Problem

• Traditional color sequential microscopy
• Slow imaging speed
• Limited real-time live cell study

Innovation

• AEMS-Net
• Deep Learning
• Cellular Imaging