Learning from Similarity Proportion Loss for Classifying Skeletal Muscle Recovery Stages

TL;DR

This paper introduces OSLSP, a weakly supervised learning method that leverages similarity proportion loss to improve classification of skeletal muscle recovery stages, addressing limitations of existing LLP methods.

Contribution

The paper proposes a novel ordinal scale learning approach using similarity proportion loss, enhancing feature extraction and classification accuracy in muscle tissue analysis.

Findings

OSLSP outperforms pre-trained models in classifying muscle recovery stages.

The method effectively incorporates ordinal information into weakly supervised learning.

Improves automated assessment of muscle regeneration with limited labeled data.

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.