Dual-stream multi-dependency graph neural network enables precise cancer survival analysis
Open Access
- 26 June 2024
- journal article
- research article
- Published by Elsevier in Medical Image Analysis
- Vol. 97, 103252
Abstract
No abstract availableKeywords
Funding Information
- The University of Tokyo (K23-2074)
- Monash University
This publication has 30 references indexed in Scilit:
- Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide imagesComputer Methods and Programs in Biomedicine, 2023
- CoADS: Cross attention based dual-space graph network for survival prediction of lung cancer using whole slide imagesComputer Methods and Programs in Biomedicine, 2023
- GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide imagesComputer Methods and Programs in Biomedicine, 2023
- Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological imagesMedical Image Analysis, 2023
- A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide imagesMedical Image Analysis, 2022
- Data-efficient and weakly supervised computational pathology on whole-slide imagesNature Biomedical Engineering, 2021
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology AssociationThe Journal of Pathology, 2019
- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image featuresNature Communications, 2016
- Automated classification of brain tumor type in whole-slide digital pathology images using local representative tilesMedical Image Analysis, 2016
- The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysisBritish Journal of Cancer, 2011