Assessment of algorithms for mitosis detection in breast cancer histopathology images
Tarih
2015-02Yazar
Veta, MitkoVan Diest, Paul J.
Willems, Stefan Martin
Wang, Haibo
Madabhushi, Anant
Cruz-Roa, Angel
Gonzalez, Fabio
Larsen, Anders Boesen Lindbo
Vestergaard, Jacob Schack Chack
Dahl, Anders Bjorholm
Cireşan, Dan Claudiu
Schmidhuber, Jürgen U.
Giusti, Alessandro
Gambardella, Luca M.
Tek, Faik Boray
Walter, Thomas C.
Wang, Chingwei
Kondo, Satoshi
Matuszewski, Bogdan J.
Precioso, Frédéric
Snell, Violet
Kittler, Josef
De Campos, Teofilo E.
Khan, Adnan M.
Rajpoot, Nasir Mahmood
Arkoumani, Evdokia
Lacle, Miangela M.
Viergever, Max A.
Pluim, Josien P W
Üst veri
Tüm öğe kaydını gösterKünye
Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, A., . . . Pluim, J. P. W. (2015). Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis, 20(1), 237-248. doi:10.1016/j.media.2014.11.010Özet
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 results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.