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Table 6 Different pooling methods for top 10 terms in all stages

From: Image-level and group-level models for Drosophilagene expression pattern annotation

Stages

Max pooling

 

Accuracy

AUC

Sensitivity

Specificity

4-6

75.43

0.7320

0.7002

0.7637

7-8

77.28

0.7616

0.7311

0.7921

9-10

74.46

0.7457

0.7397

0.7517

11-12

78.51

0.7893

0.8015

0.7771

13-17

80.96

0.7952

0.7664

0.8240

Stages

Average pooling

 

Accuracy

AUC

Sensitivity

Specificity

4-6

75.21

0.7315

0.6976

0.7654

7-8

76.80

0.7603

0.7332

0.7874

9-10

74.32

0.7439

0.7397

0.7480

11-12

77.49

0.7828

0.8034

0.7623

13-17

80.12

0.7842

0.7521

0.8164

Stages

Sqrt pooling

 

Accuracy

AUC

Sensitivity

Specificity

4-6

75.73

0.7360

0.7031

0.7692

7-8

77.49

0.7664

0.7373

0.7955

9-10

74.58

0.7459

0.7380

0.7538

11-12

78.37

0.7907

0.8094

0.7720

13-17

80.73

0.7936

0.7681

0.8191

  1. In our experiment, all the three pooling methods produce comparable performance. In particular, max pooling and Sqrt pooling perform slightly better than average pooling.
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BMC Bioinformatics

ISSN: 1471-2105

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