Table 2 Performance of the 10 CPM-Net and 3D-CNN-TR based pipelines (with a fixed confidence threshold of 0.8) and post-processing methods 1–5, for the private and RSNA datasets.

From: Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models

   

None

Method 1

Method 2

Method 3

Method 4

Method 5

Private

CPM-Net

TP

139

139

129

139

129

139

FP

(FP/case)

126

(0.88)

98

(0.69)

43

(0.30)

48

(0.34)

33

(0.23)

37

(0.26)

FN

79

79

89

79

79

79

3D-CNN-TR

TP

179

179

169

179

169

179

FP

(FP/case)

182

(1.27)

104

(0.73)

98

(0.69)

116

(0.81)

79

(0.55)

88

(0.62)

FN

39

39

49

39

39

39

RSNA

CPM-Net

TP

791

790

691

767

690

766

FP

(FP/case)

748 (0.89)

513

(0.61)

499

(0.59)

517

(0.61)

304

(0.36)

315

(0.37)

FN

236

237

366

260

337

261

3D-CNN-TR

TP

940

936

829

917

827

914

FP

(FP/case)

1,552

(1.84)

1,028

(1.22)

979

(1.16)

1,178

(1.40)

799

(0.95)

872

(1.03)

FN

87

91

198

110

200

113