A Novel Framework for Coarse-Grained Semantic Segmentation of Whole-Slide Images
- 25 July 2022
- book chapter
- Published by Springer Nature
- p. 425-439
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Micro-Net: A unified model for segmentation of various objects in microscopy imagesMedical Image Analysis, 2019
- Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology ImagesCell Reports, 2018
- A bottom-up approach for tumour differentiation in whole slide images of lung adenocarcinoma2018
- Densely Connected Convolutional Networks2017
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Deep Residual Learning for Image Recognition2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- Fully convolutional networks for semantic segmentation2015
- Oral epithelial dysplasia classification systems: predictive value, utility, weaknesses and scope for improvementJournal of Oral Pathology & Medicine, 2008
- Uncertainty and inference in the visual systemIEEE Transactions on Systems, Man, and Cybernetics, 1988