2013
DOI: 10.1117/12.2006626
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Detecting mitotic figures in breast cancer histopathology images

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Cited by 36 publications
(21 citation statements)
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“…Other approaches [18,8,12,20] address this issue by first detecting all nuclei, then classifying each nucleus separately as mitotic or non-mitotic. We follow a different, simpler approach, which does not need any additional ground-truth information and relies on a single trained detector.…”
Section: Materials Experiments and Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Other approaches [18,8,12,20] address this issue by first detecting all nuclei, then classifying each nucleus separately as mitotic or non-mitotic. We follow a different, simpler approach, which does not need any additional ground-truth information and relies on a single trained detector.…”
Section: Materials Experiments and Resultsmentioning
confidence: 99%
“…We also report the performance of faster but less accurate versions of our approach, namely: DNNf12, which averages the results of nets DNN1 and DNN2 without computing input variations (1 minute per image), and DNNf1, which is computed only from the result of DNN1 (31s per image). [12] 0.74 0.59 0.659 DREXEL 0.14 0.21 0.172 UTRECHT [20] 0.51 0.68 0.583 BII 0.10 0.32 0.156 WARWICK [10] 0.46 0.57 0.513 QATAR 0.00 0.94 0.005 Fig. 2.…”
Section: Performance and Comparisonmentioning
confidence: 99%
“…We study how top-performing mitosis detection algorithms [4,5,6,7,8] in the recent ICPR2012 mitosis detection contest [9] compare with the performance of humans which are new to the problem. We design an user test that places such humans in the same conditions as algorithms (i.e.…”
Section: Introductionmentioning
confidence: 99%
“…The NEC team [21] and CCIPD/MINDLAB team [19] employed the learned CNN-derived features for mitosis detection. The UTRECHT team extracted size, shape, color and texture features of candidate objects for automatically detecting mitotic figures [66]. The WARWICK approach modeled the pixel intensities of mitosis by a Gamma-Gaussian mixture model in conjunction with the SVM classifier [44].…”
Section: Comparative Strategiesmentioning
confidence: 99%