面向弱标注生物成像数据的模型无关知识增强机器学习
Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data
AI总结：
本文提出模型无关知识增强方法，结合掩码自编码器自监督学习提升弱标注生物成像数据分类性能，揭示ALS相关生物学效应，助力体外细胞图像分析以研究ALS病理与治疗。
摘要：
机器学习模型越来越多地用于从弱标注生物成像数据（如显微镜图像和细胞形态学图像）中自动提取特征。由于仅依赖自动特征提取，现有方法在不同弱标注数据集上的分类性能不一致。本文提出一种模型无关的知识增强方法，使机器学习模型能够利用自动特征提取无法捕获的领域知识。该方法可提升七种机器学习架构的分类性能；当与基于掩码自编码器的自监督学习结合时，取得最强的整体性能：在三个内部显微镜数据集上，比次优方法的精度提升2.9%；在三个公开细胞形态学基准上，平均精度提升6.8%。我们利用三个独立的人诱导多能干细胞来源运动神经元显微镜数据集，进行核水平分类，研究TAR DNA结合蛋白43（TDP-43）从细胞核到细胞质的错位表型，该表型与肌萎缩侧索硬化（ALS）相关。大规模准确定量该表型仍具挑战性，可能导致不同研究间的不一致。将性能最佳的模型应用于这些显微镜数据集，揭示了与ALS相关的生物学解释，包括蛋白酶体应激剂MG132和氧化应激剂亚砷酸钠（SA）对TDP-43错位的剂量依赖性效应、散发性和家族性ALS供体运动神经元的病理差异，以及依达拉奉和尼洛酰胺两种药物对SA诱导的TDP-43错位无保护作用。综上，我们的流程可实现体外细胞图像分析，以加深对ALS相关病理及潜在治疗方法的理解。
英文摘要：
Machine learning models are increasingly used for automated feature extraction from weakly annotated bioimaging data, such as microscopy and cytomorphology images. Due to the sole dependency of automated feature extraction, existing methods yield inconsistent classification performance across weakly annotated datasets. Here, we propose a model-agnostic knowledge augmentation method that enables ML models to exploit domain knowledge beyond what automated feature extraction can capture. The proposed approach improves classification performance across seven ML architectures. When integrated with masked autoencoder-based self-supervised learning, our method achieves the strongest overall performance, improving precision by 2.9% over the next-best method on three in-house microscopy datasets and by an average of 6.8% across three public cytomorphology benchmarks. We perform nucleus-level classification based on mislocalization of TAR DNA-binding protein 43 (TDP-43) from the nucleus to the cytoplasm, a phenotype linked to Amyotrophic Lateral Sclerosis (ALS), using three independent microscopy datasets of motor neurons derived from human induced pluripotent stem cells. Accurate quantification of this phenotype at scale remains challenging and may contribute to inconsistencies across studies. Application of the best-performing model to these microscopy datasets reveals biological interpretations related to ALS, including dose-dependent effects of the proteasomal stressor, MG132, and the oxidative stressor, sodium arsenite (SA), on TDP-43 mislocalization, variable pathology in motor neurons from sporadic and familial ALS donors, and a lack of protection of the pharmacological compounds, edaravone and niclosamide, against SA-induced TDP-43 mislocalization. Together, our pipeline enables in-vitro cellular image analysis to advance understanding of ALS-associated pathology and potential treatments.
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