标题： 可解释的深度学习揭示了多结构荧光成像：迈向显微镜中可信赖的人工智能的道路 显示英文标题

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

摘要： 对多种亚细胞结构的活细胞成像对于理解亚细胞动态至关重要。 然而，传统的多色顺序荧光显微镜存在显著的成像延迟和有限的亚细胞结构单独标记数量，导致实时活细胞研究应用存在重大限制。 在此，我们提出了自适应可解释多结构网络（AEMS-Net），这是一种深度学习框架，能够从单张图像中同时预测两种亚细胞结构。 该模型通过集成注意力机制和亮度适应层来标准化染色强度并优先考虑关键图像特征。 利用Kolmogorov-Arnold表示定理，我们的模型将学习到的特征分解为可解释的一元函数，增强了复杂亚细胞形态的可解释性。 我们证明，AEMS-Net允许实时记录线粒体和微管之间的相互作用，只需传统顺序通道成像程序的一半。 值得注意的是，这种方法相比传统深度学习方法在成像质量上提高了30%以上，建立了一种新的长期、可解释的活细胞成像范式，提升了探索亚细胞动态的能力。 显示英文摘要

摘要： 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.