Lupus Nephritis classification has historically relied on labor-intensive and meticulous glomerular-level labeling of renal structures in whole slide images (WSIs). However, this approach presents a formidable challenge due to its tedious and resource-intensive nature, limiting its scalability and practicality in clinical settings. In response to this challenge, our work introduces a novel methodology that utilizes only slide-level labels, eliminating the need for granular glomerular-level labeling. A comprehensive multi-stained lupus nephritis digital histopathology WSI dataset was created from the Indian population, which is the largest of its kind. LupusNet, a deep learning MIL-based model, was developed for the subtype classification of LN. The results underscore its effectiveness, achieving an AUC score of 91.0%, an F1-score of 77.3%, and an accuracy of 81.1% on our dataset in distinguishing membranous and diffused classes of LN.

The authors have declared no competing interest.

This study was funded by IHub-Data, IIIT Hyderabad (H1-002)

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics Committee of Nizam's Institute Of Medical Sciences, Hyderabad, India, gave ethical approval for this work

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All data produced in the present study are available upon reasonable request to the authors

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