Learning from Similarity Proportion Loss for Classifying Skeletal Muscle Recovery Stages
Journal:
arXiv
Published Date:
May 7, 2025
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.
