Lupus Nephritis Subtype Classification with only Slide Level Labels

Amit Sharma, Ekansh Chauhan, Megha S Uppin, Liza Rajasekhar, C.V. Jawahar, P K Vinod

Keywords: Lupus Nephritis, Weakly Supervised Learning, Whole Slide Image, Binary Classification, Histopathology

Abstract: 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 sub- type 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.

Submission Number: 132