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Assessment of algorithms for mitosis detection in breast cancer histopathology images

Open access
Veta, Mitko, van Diest, Paul J., Willems, Stefan M., Wang, Haibo, Madabhushi, Anant, Cruz-Roa, Angel, Gonzalez, Fabio, Larsen, Anders B.L., Vestergaard, Jacob S., Dahl, Anders B., Cireşan, Dan C., Schmidhuber, Jürgen, Giusti, Alessandro, Gambardella, Luca M., Tek, F. Boray, Walter, Thomas, Wang, Ching-Wei, Kondo, Satoshi, Matuszewski, Bogdan J., Precioso, Frederic, Snell, Violet, Kittler, Josef, de Campos, Teofilo E., Khan, Adnan M., Rajpoot, Nasir M., Arkoumani, Evdokia, Lacle, Miangela M., Viergever, Max A., Pluim, Josien P.W.·2015·Medical Image Analysis

Abstract

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.

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Metadata

Authors
Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A. B. L., Vestergaard, J. S., Dahl, A. B., Cireşan, D. C., Schmidhuber, J., Giusti, A., Gambardella, L. M., Tek, F. B., Walter, T., Wang, C.-W., Kondo, S., Matuszewski, B. J., Precioso, F., Snell, V., Kittler, J., de Campos, T. E., Khan, A. M., Rajpoot, N. M., Arkoumani, E., Lacle, M. M., Viergever, M. A., & Pluim, J. P. W.
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2015-02
Volume
20
Issue
1
Pages
237-248
ISSN
1361-8415 (ISSN-L)
OpenAlex
W2132031490
Cited by
480
References
0
FWCI
27.73
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Assessment of algorithms for mitosis detection in breast cancer histopathology images. https://app.cochat.ai/papers/10.1016/j.media.2014.11.010
The paper
Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A. B. L., Vestergaard, J. S., Dahl, A. B., Cireşan, D. C., Schmidhuber, J., Giusti, A., Gambardella, L. M., Tek, F. B., Walter, T., Wang, C.-W., Kondo, S., Matuszewski, B. J., . . . Pluim, J. P. W. (2015). Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis. https://doi.org/10.1016/j.media.2014.11.010

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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