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J Pathol Inform. 2013 May 30;4:9. doi: 10.4103/2153-3539.112694. Print 2013.

Classification of mitotic figures with convolutional neural networks and seeded blob features.

Author information

1
Department of Machine Learning, NEC Laboratories, America 4 Independence Way, Suite 200, Princeton, NJ 08540, USA.

Abstract

BACKGROUND:

The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR) challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral).

METHODS:

Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN). The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds.

RESULTS:

On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner.

CONCLUSIONS:

We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.

KEYWORDS:

Mitosis; convolutional neural network; digital pathology

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