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Big-Hypergraph Factorization Neural Network for Survival Prediction from Whole Slide Image
journal contributionposted on 2022-11-25, 05:09 authored by D DI, Jun ZhangJun Zhang, F Lei, Q Tian, Y Gao
Survival prediction for patients based on histopa- thological whole-slide images (WSIs) has attracted increasing attention in recent years. Due to the massive pixel data in a single WSI, fully exploiting cell-level structural information (e.g., stromal/tumor microenvironment) from the gigapixel WSI is challenging. Most of the current studies resolve the problem by sampling limited image patches to construct a graph-based model (e.g., hypergraph). However, the sampling scale is a critical bottleneck since it is a fundamental obstacle of broadening samples for transductive learning. To overcome the limitation of the sampling scale for constructing a big hypergraph model, we propose a factorization neural network that embeds the correlation among large-scale vertices and hyperedges into two low-dimensional latent semantic spaces separately, empowering the dense sampling. Thanks to the compressed low-dimensional correlation embedding, the hypergraph convolutional layers generate the high-order global representation for each WSI. To minimize the effect of the uncertainty data as well as to achieve the metric-driven learning, we also propose a multi-level ranking supervision to enable the network learning by a queue of patients on the global horizon. Extensive experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST), and the quantitative results demonstrate the proposed method outperforms state-of-the-art methods across-the-board.
JournalIEEE Transactions on Image Processing
Publication classificationC1.1 Refereed article in a scholarly journal
Computer ScienceComputer Science, Artificial IntelligenceConvolutional neural networksCorrelationData modelsEngineeringEngineering, Electrical & ElectronicFeature extractionHazardsHypergraph learningneural networkPredictive modelsREGRESSIONScience & Technologysurvival predictionTechnologyVARIABLE SELECTIONVisualizationwhole slide imageHumansNeural Networks, ComputerCognitive SciencesArtificial Intelligence and Image ProcessingElectrical and Electronic Engineering not elsewhere classified