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Identifying histological elements with convolutional neural networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83841

Miller M, Burger,  HC
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Malon, H., Miller M, Burger, H., Cosatto, E., & Graf, H. (2008). Identifying histological elements with convolutional neural networks. In 5th International Conference on Soft Computing as Transdisciplinary Science and Technology (CSTST '08) (pp. 450-456). New York, NY, USA: ACM Press.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C6C1-5
Zusammenfassung
Histological analysis on stained biopsy samples requires recognizing many kinds of local and structural details, with some awareness of context. Machine learning algorithms such as convolutional networks can be powerful tools for such problems, but often there may not be enough training data to exploit them to their full potential. In this paper, we show how convolutional networks can be combined with appropriate image analysis to achieve high accuracies on three very different tasks in breast and gastric cancer grading, despite the challenge of limited training data. The three problems are to count mitotic figures in the breast, to recognize epithelial layers in the stomach, and to detect signet ring cells.