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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.

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



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).


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.


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.


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.


Mitosis; convolutional neural network; digital pathology

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