title: Classification of mitotic figures with convolutional neural networks and seeded blob features.
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.
authors.affiliation: Department of Machine Learning, NEC Laboratories, America 4 Independence Way, Suite 200, Princeton, NJ 08540, USA.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Malon
authors.forename: Eric
authors.initials: E
authors.lastname: Cosatto
keywords: Mitosis; convolutional neural network; digital pathology