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Document type:
Zeitschriftenaufsatz 
Author(s):
Albarqouni, S.; Baur, C.; Achilles, F.; Belagiannis, V.; Demirci, S.; Navab, N. 
Title:
AggNet: Deep Learning from Crowds for Mitosis Detection in Breast Cancer Histology Images 
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 anno...    »
 
Keywords:
biological organs; cancer; data aggregation; image classification; image denoising; learning (artificial intelligence); medical image processing; biomedical image database; breast cancer histology imaging; conventional machine-learning methods; convolutional neural network; crowd annotation datasets; crowd flower API; crowd sourcing layer; data aggregation; deep CNN learning; ground-truth data; image annotation tasks; learning annotation models; learning process; mitosis detection; noisy annotat...    »
 
Journal title:
IEEE Transactions on Medical Imaging 
Year:
2016 
Journal volume:
35 
Journal issue:
Pages contribution:
1313--1321 
Print-ISSN:
0278-0062