Abstract
Evaluating the regeneration process of damaged muscle tissue is a fundamental analysis in muscle research to measure experimental effect sizes and uncover mechanisms behind muscle weakness due to aging and disease. The conventional approach to assessing muscle tissue regeneration involves whole-slide imaging and expert visual inspection of the recovery stages based on the morphological information of cells and fibers. There is a need to replace these tasks with automated methods incorporating machine learning techniques to ensure a quantitative and objective analysis. Given the limited availability of fully labeled data, a possible approach is Learning from Label Proportions (LLP), a weakly supervised learning method using class label proportions. However, current LLP methods have two limitations: (1) they cannot adapt the feature extractor for muscle tissues, and (2) they treat the classes representing recovery stages and cell morphological changes as nominal, resulting in the loss of ordinal information. To address these issues, we propose Ordinal Scale Learning from Similarity Proportion (OSLSP), which uses a similarity proportion loss derived from two bag combinations. OSLSP can update the feature extractor by using class proportion attention to the ordinal scale of the class. Our model with OSLSP outperforms large-scale pre-trained and fine-tuning models in classification tasks of skeletal muscle recovery stages.
Abstract (translated)
评估受损肌肉组织再生过程是肌体研究中的基础分析，旨在衡量实验效应大小并揭示因衰老和疾病导致的肌肉无力背后的机制。传统上，评估肌肉组织再生的方法包括整体幻灯片成像以及基于细胞和纤维形态信息的专业人员视觉检查。为了确保定量且客观的分析，有必要用结合机器学习技术的自动化方法取代这些任务。鉴于完全标注数据的有限可用性，一种可能的方法是使用类标签比例进行弱监督学习（LLP）。然而，当前的LLP方法存在两个局限：(1) 无法根据肌肉组织调整特征提取器；(2) 将表示恢复阶段和细胞形态变化的类别视为名义类别，从而丧失了序数信息。为了解决这些问题，我们提出了从相似性比例学习（OSLSP），该方法使用由两种袋组合推导出的相似性比例损失函数。OSLSP能够通过类比例注意力更新特征提取器以适应有序尺度上的变化。在骨骼肌恢复阶段分类任务中，我们的模型结合了OSLSP，在性能上超过了大规模预训练和微调模型。
URL
https://arxiv.org/abs/2505.04150
