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