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

• URL: http://arxiv.org/abs/2501.05490v1
• Date: Thu, 09 Jan 2025 07:36:28 GMT
• Title: Interpretable deep learning illuminates multiple structures fluorescence imaging: a path toward trustworthy artificial intelligence in microscopy
• Authors: Mingyang Chen, Luhong Jin, Xuwei Xuan, Defu Yang, Yun Cheng, Ju Zhang,
• Abstract summary: 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. We demonstrate that AEMS-Net allows real-time recording of interactions between mitochondria and microtubules, requiring only half the conventional sequential-channel imaging procedures.
• Score: 10.395551533758358
• License:
• 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.

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