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<p>This study introduces PS-HSOM, a patch-shared hierarchical Self-Organizing Map, and evaluates its unsupervised representation learning performance through post hoc cluster-to-label evaluation across diverse application domains. When applied to the MNIST dataset, the model attains 90.5% test accuracy after post hoc cluster-to-label mapping using only 4000 training samples and three training epochs per layer. This result exceeds the performance of E-DSOM by 3.4%, while requiring approximately 1.33<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> more training data, but approximately two orders of magnitude fewer parameters, and offering full experimental reproducibility through fixed random seed initialization. The framework is further validated on a clinical embryo assessment task using the Linz IVF dataset, comprising 1615 training images and 214 test images. In this setting, PS-HSOM achieves 97.2% accuracy in blastocyst expansion stage (EXP) classification without employing morphological labels during feature learning. These findings suggest that PS-HSOM can achieve clinically promising performance for unsupervised embryo grading under limited-data conditions.</p>

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Unsupervised image classification with patch shared hierarchical SOM on MNIST and for clinical blastocyst quality assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月5日

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  • Magnus Johnsson,
  • Zeinab Shahbazi,
  • Reihaneh Tarlani,
  • Fredrik Frisk

摘要

This study introduces PS-HSOM, a patch-shared hierarchical Self-Organizing Map, and evaluates its unsupervised representation learning performance through post hoc cluster-to-label evaluation across diverse application domains. When applied to the MNIST dataset, the model attains 90.5% test accuracy after post hoc cluster-to-label mapping using only 4000 training samples and three training epochs per layer. This result exceeds the performance of E-DSOM by 3.4%, while requiring approximately 1.33 \(\times\) more training data, but approximately two orders of magnitude fewer parameters, and offering full experimental reproducibility through fixed random seed initialization. The framework is further validated on a clinical embryo assessment task using the Linz IVF dataset, comprising 1615 training images and 214 test images. In this setting, PS-HSOM achieves 97.2% accuracy in blastocyst expansion stage (EXP) classification without employing morphological labels during feature learning. These findings suggest that PS-HSOM can achieve clinically promising performance for unsupervised embryo grading under limited-data conditions.