Table 3 Detection performance of recent models on LUNA16 and Tianchi

From: GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation

Model

Task

LUNA16 detection

Tianchi detection

  

Pre.

Recall

F1

FP

Pre.

Recall

F1

FP

GLANCE (ours)

Seg+Det

92.3

94.1

93.2

7.7

95.0

93.6

94.3

5.0

LN-DETR34

Det

90.5

91.9

91.2

9.5

89.5

90.7

90.1

10.5

Santone et al.21

Seg+Det

91.2

91.5

91.3

8.8

90.1

90.9

90.5

9.9

NDLA20

Det

92.0

90.7

91.3

8.0

91.5

90.1

90.8

8.5

Shah et al.35

Det

91.8

90.1

90.9

8.2

89.9

88.5

89.2

10.1

Sui et al.36

Seg+Det

87.2

89.8

88.5

12.8

85.5

88.1

86.8

14.5

UrRehman et al.37

Det

89.5

88.7

89.1

10.5

87.8

87.0

87.4

12.2

Moturi et al.38

Det

84.5

88.2

86.3

15.5

82.9

86.5

84.7

17.1

Alhajim et al.26

Seg+Det

88.1

87.9

88.0

11.9

87.0

86.8

86.9

13.0

AWEU-Net27

Seg+Det

90.1

87.5

88.8

9.9

88.9

86.2

87.5

11.1

Lung-CADex39

Det

88.5

86.0

87.2

11.5

88.2

85.8

87.0

11.8

CSE-GAN40

Det

86.9

85.5

86.2

13.1

84.5

83.9

84.2

15.5

EHO-Deep CNN41

Det

86.2

85.1

85.6

13.8

84.0

83.3

83.6

16.0

NHNN42

Det

75.3

72.1

73.7

24.7

73.8

70.5

72.1

26.2

DeepSEED43

Det

87.1

88.2

87.6

12.9

86.7

87.8

87.2

13.3

3D RPN44

Det

91.0

91.6

91.3

9.0

93.2

92.4

92.8

6.8

Lu et al.45

Det

86.0

87.5

86.7

14.0

85.6

86.4

86.0

14.4

NoduleNet46

Seg+Det

88.3

87.4

87.8

11.7

86.1

86.8

86.4

13.9

  1. Metrics are precision, recall, F1-score and false precision. Tianchi contains center coordinates and diameters only and is thus suitable for detection but not segmentation.