AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
German Center for Neurodegenerative Diseases · Technical University of Munich · +3 more institutions
Abstract
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy…
Citation impact
- FWCI
- 96.30
- Percentile
- 100%
- References
- 49
Authors
6- SAShadi AlbarqouniCorresponding
German Center for Neurodegenerative Diseases, Technical University of Munich
- CBChristoph Baur
Technical University of Munich
- FAFelix Achilles
Technical University of Munich
- VBVasileios Belagiannis
Technical University of Munich, University of Oxford, Oxford Research Group
- SDStefanie Demirci
Technical University of Munich
Topics & keywords
- Crowdsourcing
- Computer science
- Annotation
- Deep learning
- Convolutional neural network
- Artificial intelligence
- Crowds
- Machine learning