One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification
Open Access
- 11 November 2022
- journal article
- research article
- Published by Elsevier in Medical Image Analysis
- Vol. 83, 102685
Abstract
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Funding Information
- Alan Turing Institute
- UK Research and Innovation
- Medical Research Council (MR/P015476/1)
This publication has 36 references indexed in Scilit:
- Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology ImagesScientific Reports, 2017
- DCAN: Deep contour-aware networks for object instance segmentation from histology imagesMedical Image Analysis, 2017
- Gland segmentation in colon histology images: The glas challenge contestMedical Image Analysis, 2017
- Multi-class texture analysis in colorectal cancer histologyScientific Reports, 2016
- Deep Residual Learning for Image Recognition2016
- Cross-Stitch Networks for Multi-task Learning2016
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification2015
- Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser2015
- A Review on Multi-Label Learning AlgorithmsIEEE Transactions on Knowledge and Data Engineering, 2013
- Measures of the Amount of Ecologic Association Between SpeciesEcology, 1945