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Abstract: FR-OR55

Explainability of a Deep Learning Based Classification Model for ANCA-Associated Glomerulonephritis

Session Information

Category: Pathology and Lab Medicine

  • 1700 Pathology and Lab Medicine

Authors

  • Wester Trejo, Maria, Leiden University Medical Center, Department of Pathology, Leiden, Netherlands
  • Sadeghi, Maryam, Medical University of Innsbruck, Department of Medical Statistics, Informatics and Health Economics, Innsbruck, Austria
  • Singh, Shivam, Medical University of Innsbruck, Department of Medical Statistics, Informatics and Health Economics, Innsbruck, Austria
  • Kronbichler, Andreas, University of Cambridge, Department of Medicine, Cambridge, United Kingdom
  • Tesar, Vladimir, Charles University and General University Hospital, Department of Nephrology, Prague, Czechia
  • Puéchal, Xavier, Hôpital Cochin, Department of Internal Medicine, Paris, France
  • Goebel, Georg, Medical University of Innsbruck, Department of Medical Statistics, Informatics and Health Economics, Innsbruck, Austria
  • Bajema, Ingeborg M., Leiden University Medical Center, Department of Pathology, Leiden, Netherlands
Background

The histopathological classification for ANCA-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in renal biopsies of patients with ANCA-associated vasculitis. As with many fields, medicine saw a rapid emergence of Artificial Intelligence (AI) and Deep Learning (DL) approaches. In the field of digital pathology, AI can now serve as decision-support for pathologists, with the potential for gains in productivity and time-saving. It was demonstrated previously that AI can aid in identifying histopathological classes of renal diseases, e.g. of diabetic nephropathy. Although these models reach high prediction accuracies, their black box structure makes them very non-transparent. The disadvantage is that the networks’ decisions are not easily interpretable by humans and it is not clear what information in the input data underlies their decisions. This necessitates the use of Explainable AI (XAI), so that decisions made by AI models become accessible for validation by a human expert.

Methods

Renal biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic renal biopsy between 1991 and 2011, were included. On the scanned slides glomeruli were labelled as ‘normal’, ‘sclerotic’, ‘crescentic’ or ‘abnormal - other’. We developed a DL-based computational pipeline, which detects and classifies the glomeruli. We investigated the explainability of our model, using XAI techniques to shed light on the decision-making criteria of our trained DL classifier, using saliency maps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model.

Results

Our DL model shows a prediction accuracy of 93% for classifying glomeruli. The saliency maps from our trained DL models help us to better understand the decision-making criteria of the DL black box.

Conclusion

AI and DL play an increasingly important role in (nephro)pathology. To ultimately enable safe implementation of these models in clinical practice, validation of their decisions is needed. To achieve this, we used XAI techniques, which showed great potential for illuminating the decision-making criteria of the DL black box.