论文标题
SEMPAI：一种自我增强的多光子人工智能，用于对肌肉功能和病理的提前评估
SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology
论文作者
论文摘要
深度学习（DL）在生物医学研究中取得了显着的成功。但是，大多数DL算法用作黑匣子，排除生物医学专家，并且需要大量数据。我们介绍了自我增强的多光子人工智能（SEMPAI），该智力（SEMPAI）将假设驱动的先验整合在数据驱动的DL方法中，以研究肌肉纤维的多光子显微镜显微镜（MPM）。 Sempai利用元学习来优化先前的集成，数据表示和神经网络体系结构。这允许假设检验，并提供有关MPM图像中生物信息起源的可解释反馈。 SEMPAI执行几个任务的联合学习，以实现对小数据集的预测。该方法应用于广泛的多研究数据集上，从而对单个肌肉纤维的病理和功能进行了最大的关节分析。 Sempai在七个预测任务中的六个（包括数据稀缺的任务中的六个）中优于最先进的生物标志物。 Sempai的具有集成先验的DL模型优于没有先验的DL模型，并且比先前的机器学习方法优越。
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biological information in MPM images. SEMPAI performs joint learning of several tasks to enable prediction for small datasets. The method is applied on an extensive multi-study dataset resulting in the largest joint analysis of pathologies and function for single muscle fibers. SEMPAI outperforms state-of-the-art biomarkers in six of seven predictive tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only machine learning approaches.
