. Author manuscript; available in PMC: 2022 Jan 18.

Published in final edited form as: Conf Comput Vis Pattern Recognit Workshops. 2021 Nov 13;2021:14318–14328. doi: 10.1109/CVPR46437.2021.01409

Table 5.

Performance comparison on classical MIL dataset. Experiments were run 5 times each with a 10-fold cross-validation. The mean and standard deviation of the classification accuracy is reported (mean ± std). mi-Net[52], MI-Net [52], MI-Net with DS [52], MI-Net with RC [52], ABMILP [22], ABMILP-Gated [22], GNN-MIL [47], DP-MINN [55]. NLMIL and ANLMIL use the non-local blocks from [51] and [56]. Previous benchmark results are taken from [22, 47, 55] and the same training setting as [22] is used.

Methods	MUSK1	MUSK2	FOX	TIGER	ELEPHANT
mi-Net	0.889 ± 0.039	0.858 ± 0.049	0.613 ± 0.035	0.824 ± 0.034	0.858 ± 0.037
MI-Net	0.887 ± 0.041	0.859 ± 0.046	0.622 ± 0.038	0.830 ± 0.032	0.862 ± 0.034
MI-Net with DS	0.894 ± 0.042	0.874 ± 0.043	0.630 ± 0.037	0.845 ± 0.039	0.872 ± 0.032
MI-Net with RC	0.898 ± 0.043	0.873 ± 0.044	0.619 ± 0.047	0.836 ± 0.037	0.857 ± 0.040
ABMIL	0.892 ± 0.040	0.858 ± 0.048	0.615 ± 0.043	0.839 ± 0.022	0.868 ± 0.022
ABMIL-Gated	0.900 ± 0.050	0.863 ± 0.042	0.603 ± 0.029	0.845 ± 0.018	0.857 ± 0.027
GNN-MIL	0.917 ± 0.048	0.892 ± 0.011	0.679 ± 0.007	0.876 ± 0.015	0.903 ± 0.010
DP-MINN	0.907 ± 0.036	0.926 ± 0.043	0.655 ± 0.052	0.897 ± 0.028	0.894 ± 0.030

NLMIL	0.921 ± 0.017	0.910 ± 0.009	0.703 ± 0.035	0.857 ± 0.013	0.876 ± 0.011
ANLMIL	0.912 ± 0.009	0.822 ± 0.084	0.643 ± 0.012	0.733 ± 0.068	0.883 ± 0.014

DSMIL	0.932 ± 0.023	0.930 ± 0.020	0.729 ± 0.018	0.869 ± 0.008	0.925 ± 0.007