A survey on graph-based deep learning for computational histopathology
- 1 January 2022
- journal article
- research article
- Published by Elsevier in Computerized Medical Imaging and Graphics
- Vol. 95, 102027
Abstract
No abstract availableKeywords
All Related Versions
This publication has 45 references indexed in Scilit:
- A curated collection of tissue microarray images and clinical outcome data of prostate cancer patientsScientific Data, 2017
- A Dataset and a Technique for Generalized Nuclear Segmentation for Computational PathologyIEEE Transactions on Medical Imaging, 2017
- Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignmentPattern Recognition Letters, 2017
- A Dataset for Breast Cancer Histopathological Image ClassificationIEEE Transactions on Biomedical Engineering, 2015
- Nonsurgical Treatment of Recurrent GlioblastomaCurrent Oncology, 2015
- Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future PotentialIEEE Reviews in Biomedical Engineering, 2013
- Mutational landscape and significance across 12 major cancer typesNature, 2013
- Comprehensive molecular portraits of human breast tumoursNature, 2012
- SLIC Superpixels Compared to State-of-the-Art Superpixel MethodsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Lung cancer screening with low-dose helical CT: results from the National Lung Screening Trial (NLST)Journal of Medical Screening, 2011