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Ranking Loss based Weakly Supervised Model for Prediction of HPV Infection Status from Multi-Gigapixel Histology Images
Ruoyu Wang · Amina Asif · Raja Muhammad Saad Bashir · Ali Khurram · Nasir Rajpoot
We present a novel ranking loss based Multiple Instance Learning (rankMIL) method which uses the routine H&E-stained slide images to predict the human papillomavirus (HPV) infection status of head and neck cancer patients. Experiments were conducted on the publicly available TCGA-HNSC cohort and the proposed method achieved the new state-of-the-art performance (AUC=0.92) compared to previous methods.
Author Information
Ruoyu Wang (University of Warwick)
Amina Asif (University of Warwick)
Raja Muhammad Saad Bashir (University of Warwick)
Ali Khurram (University of Sheffield)
Nasir Rajpoot (University of Warwick)
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