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Table 3 Summary of the bone age testing methods based on deep learning

From: Fine-grained precise-bone age assessment by integrating prior knowledge and recursive feature pyramid network

Category of the method

References

Method

Dataset

Resolution of label

MAE (month)

End-to-end

Alexander et al. [28]

Inception-V3 + DenseNet; fine-tuned ResNet-50; ice module; U-Net segmentation and CNN-based recognition

RSNA dataset

Total data: 14,236

Training set: validation set: test set = 12,611:1425:200

The ratio of male to female is close to 1:1

1 month

4.27–4.5 months

Ren et al. [3]

Regression CNN based on inception-V3

RSNA dataset

SCH data set (Shanghai Children’s Hospital): total data: 12,390

Training set: validation set: test set = 9912:1239:1239

1 month

5.2 months

Chen et al. [10]

ResNet + spatial transformer + LBP + GLCM + SVM

Total data: 12,536 from Shengjing Hospital, Training set: test set = 85:15

Not mentioned

5.5 months

Mutasa et al. [29]

ResNet and inception

Total data: 20,581 (10,289 from the network, 8909 from Columbia University Hospital, 1383 from the public data set)

Training set: validation set: test set = 11,007:1105:300

1 month

6.43 months

Liu et al. [30]

NSCT-based multi-scale CNN (VGG16)

Digital Hand Atlas Database System

Total data: 1391

12 months (1 year)

6.43 months

Iglovikov et al. [47]

U-Net + VGG-style + linear combination

RSNA dataset

1 month

7.52 months

Hu et al. [48]

AlexNet

DR images of Uygur people, total data: 472

Training set: test set = 7:3

1 month

8.40 months

Lee et al. [16]

Caffenet architecture

Total data: 600

Training set: test set = 2:1

Not mentioned

18.9 months

Synho et al. [18]

CNN segmentation + Lenet BAA

Training set: verification set: test set = 70:15:15

12 months (1 year)

Accuracy of MAE < 1 year: 92.29%

Tajmir et al. [17]

LeNet-5

Total data: 8325

Training set: test set = 8045:280

12 months (1 year)

Accuracy of MAE < 1 year: 98.6%

Subregion-based

Son et al. [32]

faster R-CNN + VGG

Total data: 3344

Training set: test set = 4:1

Age: 2–14 years old

Not mentioned

5.52 months

Reject rate: 1.6%

Bui et al. [20]

faster R-CNN + TW3

Total data: 1375

Training set: test set = 4:1

12 months (1 year)

7.08 months

Spampinato et al. [9]

BoNet (ad-hoc CNN, 6 layers)

Digital Hand Atlas Database System

Total data: 1391

12 months (1 year)

9.48 months

Proposed method

U-Net + RPN + cascade R-CNN + DenseNet with self-attention

Total data: 2129

Annotated for fine-grained BAA: 873

Training set: test set = 4:1

Age: 2–18 years

12 months (1 year)

7.32 months

(7.08 months for age 2–18)

Reject rate: 0