bibtext: @InProceedings{ Xu_Novelty_MICCAI2024,
                   author = { Xu, Rui and Yu, Dan and Yang, Xuan and Ye, Xinchen and Wang, Zhihui and Wang, Yi and Wang, Hongkai and Li, Haojie and Huang, Dingpin and Xu, Fangyi and Gan, Yi and Tu, Yuan and Hu, Hongjie },
                   title = { { Novelty Detection Based Discriminative Multiple Instance Feature Mining to Classify NSCLC PD-L1 Status on HE-Stained Histopathological Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: It is crucial to analyze HE-stained histopathological whole slide images (WSIs) to classify PD-L1 status for non-small cell lung cancer (NSCLC) patients, due to the expensive immunohistochemical examination performed in practical clinics. Usually, a multiple instance learning (MIL) framework is applied to resolve the classification problems of WSIs. However, the existing MIL methods cannot perform well on the PD-L1 status classification, due to unlearnable instance features and challenging instances containing weak visual differences. To address this problem, we propose a novelty detection based discriminative multiple instance feature mining method. It contains a trainable instance feature encoder, learning effective information from the on-hand dataset to reduce the domain difference problem, and a novelty detection based instance feature mining mechanism, selecting typical instances to train the encoder for mining more discriminative instance features. We evaluate the proposed method on a private NSCLC PD-L1 dataset and the widely used public Camelyon16 dataset that is targeted for breast cancer identification. Experimental results show that the proposed method is not only effective in predicting NSCLC PD-L1 status but also generalized well on the public dataset.
title: Novelty Detection Based Discriminative Multiple Instance Feature Mining to Classify NSCLC PD-L1 Status on HE-Stained Histopathological Images
authors: Xu, Rui Yu, Dan Yang, Xuan Ye, Xinchen Wang, Zhihui Wang, Yi Wang, Hongkai Li, Haojie Huang, Dingpin Xu, Fangyi Gan, Yi Tu, Yuan Hu, Hongjie