Classification of Glomerulonephritis with CNN and Self-Attention Networks in Individual Glomeruli in Nephropathology

Gloria Bueno, Anibal Pedraza, Israel Mateos-Aparicio, Huy Q. Vo, Nicola Altini, Hien Van Nguyen, Jan Becker

Keywords: Classification of Glomerulonephritis, Deep Learning in Medical Diagnostics, Machine Learning in Nephropathology, Self-Attention Architectures

TL;DR: Glomerulonephritis Classification

Abstract: Despite the renal biopsy being the gold standard for diagnosing glomerulonephritis, this practice remains inaccessible for many patients worldwide. Nephropathologists typically combine microscopy, immunohistology, transmission electron microscopy, clinical information, and genetic studies for diagnosis. However, variability in nephropathology evaluation has hindered its integration with emerging technologies and personalized medicine. This study proposes the use of deep learning to extract significant features to distinguish glomerulonephritis from PAS sections without other modalities. To test this hypothesis, various AI methods were tested for classifying 12 common glomerulonephritis diagnoses. Finally, a sequential classification was implemented, initially characterizing sclerosed and non-sclerosed glomeruli using Swin-Transformers, followed by classifying the non-sclerosed glomeruli into 12 types of glomerulonephritis using ConvNeXt. The first step achieved an average Balanced Accuracy of 97% and an AUC of 0.96. In the second step, a balanced accuracy considering up to the top-3 of 79.5% and an avarage AUCs of 0.76 were achieved. This study establishes a baseline for this challenging classification task, demonstrating promising results even on single PAS glomerular crops.

Track: 4. AI-based clinical decision support systems

Registration Id: WPNMVXR5V6Z

Submission Number: 324