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
Abstract
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.
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