LupusNet

Lupus Nephritis Subtype Classification with only Slide Level Labels

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

medRxiv | Website | Cite

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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 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.

👨🏻‍💻 Code

We have adapted our pipeline from CLAM.

# Extract features from input glomeruli images
CUDA_VISIBLE_DEVICES=0,1 python extract_glom_features_fp.py --data_h5_dir DIR_TO_COORDS --data_slide_dir DATA_DIRECTORY --csv_path CSV_FILE_NAME --feat_dir FEATURES_DIRECTORY --batch_size 512 --slide_ext .svs

CUDA_VISIBLE_DEVICES=0 python run.py --drop_out --early_stopping --lr 2e-4 --k 10 --label_frac 0.5 --exp_code ln4_vs_ln5_lupusnet --weighted_sample --bag_loss ce --inst_loss svm --task ln4_vs_ln5 --model_type clam_sb --log_data --data_root_dir DATA_ROOT_DIR

📖 Cite

If our work has helped your research, please consider citing it:

@inreview{
    lupusnet,
    title={LUPUS NEPHRITIS SUBTYPE CLASSIFICATION WITH ONLY SLIDE LEVEL LABELS}, 
    DOI={10.1101/2023.12.03.23299357},
    author={Sharma, Amit and Chauhan, Ekansh and Uppin, Megha S and Rajasekhar, Liza and Jawahar, C V and Vinod, P K},
    year={2023}
}

📝 License

© This code is made available under the GPLv3 License and is available for non-commercial academic purposes.