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Table 2 Comparison of parameters (M), training time (m) and FLOPs (G) between our method and other segmentation networks, in which highlighted values represent the best result of each criteria

From: HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

Methods Parameters (M) Training time (m) FLOPs (G)
ENet 0.36 51 0.04
SegNet 53.54 62 4.5
PSPNet 85.86 80 5.05
DeeplabV3+ 59.33 71 1.38
UNet++ 9.16 42 2.16
Fast SCNN 1.2 38 0.02
DFANet 2.16 69 0.03
FANet 13.65 40 0.09
SPNet 3.8 55 0.17
Ours 3.46 70 0.16