Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation

  • Paulo Chagas UFBA
  • Luiz Souza UFBA
  • Izabelle Pontes UFBA
  • Rodrigo Calumby UFBA / UEFS
  • Michele Angelo UEFS
  • Angelo Duarte UEFS
  • Washington L. C. dos-Santos Fundação Oswaldo Cruz
  • Luciano Oliveira UFBA


Membranous Nephropathy (MN) is one of the most common glomerular diseases that cause adult nephrotic syndrome. To assist pathologists on MN classification, we evaluated three deep-learning-based architectures, namely, ResNet-18, DenseNet and Wide-ResNet. In addition, to accomplish more reliable results, we applied Monte-Carlo Dropout for uncertainty estimation. We achieved average F1-Scores above 92% for all models, with Wide-ResNet obtaining the highest average F1-Score (93.2%). For uncertainty estimation on Wide-ResNet, the uncertainty scores showed high relation with incorrect classifications, proving that these uncertainty estimates can support pathologists on the analysis of model predictions.


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CHAGAS, Paulo; SOUZA, Luiz; PONTES, Izabelle; CALUMBY, Rodrigo; ANGELO, Michele; DUARTE, Angelo; DOS-SANTOS, Washington L. C.; OLIVEIRA, Luciano. Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 257-268. ISSN 2763-8952. DOI:

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