Assessment of algorithms for mitosis detection in breast cancer histopathology images

@article{Veta2015AssessmentOA,
  title={Assessment of algorithms for mitosis detection in breast cancer histopathology images},
  author={Mitko Veta and Paul J. van Diest and Stefan M. Willems and Haibo Wang and Anant Madabhushi and Angel Cruz-Roa and Fabio A. Gonz{\'a}lez and Anders Boesen Lindbo Larsen and Jacob S. Vestergaard and Anders Bjorholm Dahl and Dan C. Ciresan and J{\"u}rgen Schmidhuber and Alessandro Giusti and Luca Maria Gambardella and F. Boray Tek and Thomas Walter and Ching-Wei Wang and Satoshi Kondo and Bogdan J. Matuszewski and Fr{\'e}d{\'e}ric Precioso and Violet Snell and Josef Kittler and Te{\'o}filo Em{\'i}dio de Campos and Adnan Mujahid Khan and Nasir M. Rajpoot and Evdokia Arkoumani and Miangela M. Lacle and Max A. Viergever and Josien P. W. Pluim},
  journal={Medical image analysis},
  year={2015},
  volume={20 1},
  pages={237-48}
}
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the… CONTINUE READING
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