bibtext: @InProceedings{ Zho_ccRCC_MICCAI2024,
                   author = { Zhou, Huijian and Tian, Zhiqiang and Han, Xiangmin and Du, Shaoyi and Gao, Yue },
                   title = { { ccRCC Metastasis Prediction via Exploring High-Order Correlations on Multiple WSIs } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Metastasis prediction based on gigapixel histopathology whole-slide images (WSIs) is crucial for early diagnosis and clinical decision-making of clear cell renal cell carcinoma (ccRCC). However, most existing methods focus on extracting task-related features from a single WSI, while ignoring the correlations among WSIs, which is important for metastasis prediction when a single patient has multiple pathological slides. In this case, we propose a multi-slice-based hypergraph computation (MSHGC) method for metastasis prediction, which considers the intra-correlations within a single WSI and cross-correlations among multiple WSIs of a single patient simultaneously. Specifically, intra-correlations are captured within both topology and semantic feature spaces, while cross-correlations are modeled between the patches from different WSIs. Finally, the attention mechanism is used to suppress the contribution of task-irrelevant patches and enhance the contribution of task-relevant patches. MSHGC achieves the C-index of 0.8441 and 0.8390 on two carcinoma datasets(namely H1 and H2), outperforming state-of-the-art methods, which demonstrates the effectiveness of the proposed MSHGC.
title: ccRCC Metastasis Prediction via Exploring High-Order Correlations on Multiple WSIs
authors: Zhou, Huijian Tian, Zhiqiang Han, Xiangmin Du, Shaoyi Gao, Yue