Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation - Chagas, Paulo; Souza, Luiz; Pontes, Izabelle; Calumby, Rodrigo; Angelo, Michele; Duarte, Angelo; dos-Santos, Washington L. C.; Oliveira, Luciano (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2021: Anais do XXI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2021.16070, 2021)
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.