Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse




Conference Paper

Identifying histological elements with convolutional neural networks


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

There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

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.

Cite as:
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.