arXiv cs.AI· Jayanie Bogahawatte, Sachith Seneviratne, Simon Maksour, Dzung Do-Ha, Elizabeth Qian, Samuel Brennan, Matthew Keon, Lezanne Ooi, Saman Halgamuge·· 6 天前AI 评分22
模型无关的知识增强机器学习方法，提升弱标注生物影像数据分类性能
Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data
AI 导读
研究者提出一种模型无关的知识增强方法，让 ML 模型利用自动特征提取之外的领域知识，在七种 ML 架构上均提升分类性能。与基于 masked autoencoder 的自监督学习结合时表现最佳，在三个内部显微镜数据集上精度比次优方法提升 2.9%，在三个公开细胞形态学基准上平均提升 6.8%。
正文 · 原文
Abstract: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.
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.13637 [eess.IV] |
| (or arXiv:2610.13637v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13637 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jayanie Bogahawatte [view email]
[v1]
Fri, 9 Oct 2026 14:17:48 UTC (11,148 KB)
来源：arXiv cs.AI · arxiv.org