CAF-AHGCN: context-aware attention fusion adaptive hypergraph convolutional network for human-interpretable prediction of gigapixel whole-slide image
- 13 February 2024
- journal article
- research article
- Published by Springer Nature in The Visual Computer
- Vol. 40 (12) , 8747-8765
Abstract
No abstract availableKeywords
Funding Information
- Natural Science Foundation of Shanxi Province (202303021211014, 202103021223411)
- Excellent Young Scientists Fund (11804209)
This publication has 43 references indexed in Scilit:
- Weakly-supervised learning for lung carcinoma classification using deep learningScientific Reports, 2020
- Capturing Cellular Topology in Multi-Gigapixel Pathology Images2020
- Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology WorkloadScientific Reports, 2020
- RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classificationMedical Image Analysis, 2019
- Patch Transformer for Multi-tagging Whole Slide Histopathology Images2019
- CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images2019
- CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation2019
- Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image AnalysisIEEE Transactions on Cybernetics, 2019
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide imagesNature Medicine, 2019
- Histopathological Image Analysis: A ReviewIEEE Reviews in Biomedical Engineering, 2009