Research Article
Representation of Differential Learning Method for Mitosis Detection
Table 2
Comparison with other state-of-the-art approaches on the Aperio-type images.
| Method | Precision | Recall | F1-score |
| STRASBOURG | — | — | 0.024 | YILDIZ | — | — | 0.167 | MINES-CURIE-INSERM | — | — | 0.235 | CUHK | 0.448 | 0.300 | 0.356 | DeepMitosis [21] | 0.431 | 0.443 | 0.437 | CasNN [17] | 0.411 | 0.478 | 0.442 | MaskMitosis [37] | 0.500 | 0.453 | 0.475 | LRCNN + in group [38] | 0.654 | 0.663 | 0.659 | Efficient mitosis detection [39] | 0.534 | 0.661 | 0.585 | SegMitos-r15R30 [5] | 0.594 | 0.512 | 0.550 | SegMitos-random [5] | 0.637 | 0.502 | 0.562 | RDLM | 0.685 | 0.70 | 0.692 |
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