Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (1): 175-181.DOI: 10.11772/j.issn.1001-9081.2023010002
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Yuning ZHANG1, Abudukelimu ABULIZI1(), Tisheng MEI2, Chun XU1, Maierdana MAIMAITIREYIMU3, Halidanmu ABUDUKELIMU1, Yutao HOU1
Received:
2023-01-04
Revised:
2023-04-13
Accepted:
2023-04-13
Online:
2023-06-06
Published:
2024-01-10
Contact:
Abudukelimu ABULIZI
About author:
ZHANG Yuning, born in 2000, M. S. candidate. Her research interests include artificial intelligence, medical artificial intelligence.Supported by:
张雨宁1, 阿布都克力木·阿布力孜1(), 梅悌胜2, 徐春1, 麦尔达娜·买买提热依木3, 哈里旦木·阿布都克里木1, 侯钰涛1
通讯作者:
阿布都克力木·阿布力孜
作者简介:
张雨宁(2000—),女,河北定州人,硕士研究生,CCF学生会员,主要研究方向:人工智能、医学人工智能;基金资助:
CLC Number:
Yuning ZHANG, Abudukelimu ABULIZI, Tisheng MEI, Chun XU, Maierdana MAIMAITIREYIMU, Halidanmu ABUDUKELIMU, Yutao HOU. Anomaly detection method for skeletal X-ray images based on self-supervised feature extraction[J]. Journal of Computer Applications, 2024, 44(1): 175-181.
张雨宁, 阿布都克力木·阿布力孜, 梅悌胜, 徐春, 麦尔达娜·买买提热依木, 哈里旦木·阿布都克里木, 侯钰涛. 基于自监督特征提取的骨骼X线影像异常检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(1): 175-181.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023010002
类型 | 训练集样本数 | 测试集样本数 | 样本总数 | ||
---|---|---|---|---|---|
正常 | 异常 | 正常 | 异常 | ||
合计 | 8 280 | 5 177 | 661 | 537 | 14 656 |
肘部 | 1 094 | 660 | 92 | 66 | 1 912 |
手指 | 1 280 | 655 | 92 | 83 | 2 110 |
手部 | 1 497 | 521 | 101 | 66 | 2 185 |
肱骨 | 321 | 271 | 68 | 67 | 727 |
前臂 | 950 | 287 | 69 | 64 | 1 010 |
肩部 | 1 364 | 1 457 | 99 | 95 | 3 015 |
手腕 | 2 134 | 1 326 | 140 | 97 | 3 697 |
Tab. 1 Distribution of normal and abnormal samples in MURA dataset
类型 | 训练集样本数 | 测试集样本数 | 样本总数 | ||
---|---|---|---|---|---|
正常 | 异常 | 正常 | 异常 | ||
合计 | 8 280 | 5 177 | 661 | 537 | 14 656 |
肘部 | 1 094 | 660 | 92 | 66 | 1 912 |
手指 | 1 280 | 655 | 92 | 83 | 2 110 |
手部 | 1 497 | 521 | 101 | 66 | 2 185 |
肱骨 | 321 | 271 | 68 | 67 | 727 |
前臂 | 950 | 287 | 69 | 64 | 1 010 |
肩部 | 1 364 | 1 457 | 99 | 95 | 3 015 |
手腕 | 2 134 | 1 326 | 140 | 97 | 3 697 |
实验参数 | 设置 | 实验参数 | 设置 |
---|---|---|---|
Patch大小 | 16 | 优化器 | AdamW |
ViT模型层数 | 12 | 教师网络优化动量 | 0.996 |
ViT模型注意力头 | 6 | 教师网络温度参数 | 0.04 |
Batch大小 | 16 | Dropout率 | 0.1 |
迭代次数 | 300 |
Tab. 2 Experiment parameter settings
实验参数 | 设置 | 实验参数 | 设置 |
---|---|---|---|
Patch大小 | 16 | 优化器 | AdamW |
ViT模型层数 | 12 | 教师网络优化动量 | 0.996 |
ViT模型注意力头 | 6 | 教师网络温度参数 | 0.04 |
Batch大小 | 16 | Dropout率 | 0.1 |
迭代次数 | 300 |
模型 | 准确率 | 模型 | 准确率 |
---|---|---|---|
Inception | 94.43 | ResNet152 | 87.86 |
VGG16 | 93.50 | DenseNet121 | 95.44 |
VGG19 | 89.84 | DenseNet169 | 76.35 |
Xception | 95.78 | DenseNet201 | 65.99 |
ResNet50 | 95.07 | DINO(ViT) | 95.50 |
ResNet101 | 94.71 |
Tab. 3 Acuracies of skeletal classification at first stage by different models
模型 | 准确率 | 模型 | 准确率 |
---|---|---|---|
Inception | 94.43 | ResNet152 | 87.86 |
VGG16 | 93.50 | DenseNet121 | 95.44 |
VGG19 | 89.84 | DenseNet169 | 76.35 |
Xception | 95.78 | DenseNet201 | 65.99 |
ResNet50 | 95.07 | DINO(ViT) | 95.50 |
ResNet101 | 94.71 |
模型 | 准确率 | 平均准确率 | ||||||
---|---|---|---|---|---|---|---|---|
肩膀 | 肘部 | 手指 | 前臂 | 手部 | 肱骨 | 手腕 | ||
Inception | 79.00 | 82.97 | 60.32 | 65.61 | 88.16 | 80.38 | 71.09 | 75.36 |
VGG16 | 50.00 | 65.00 | 70.00 | 89.00 | 81.36 | 56.94 | 73.93 | 69.46 |
VGG19 | 50.00 | 60.00 | 75.00 | 87.00 | 80.70 | 56.94 | 84.00 | 70.52 |
Xception | 81.00 | 78.45 | 69.74 | 68.58 | 79.61 | 56.60 | 75.00 | 72.71 |
ResNet50 | 79.00 | 69.18 | 64.69 | 78.72 | 85.75 | 77.78 | 74.09 | 75.60 |
ResNet101 | 80.00 | 74.78 | 63.82 | 62.84 | 81.14 | 80.21 | 77.13 | 74.27 |
ResNet152 | 76.00 | 72.84 | 69.52 | 70.72 | 78.07 | 78.82 | 74.70 | 74.31 |
DenseNet121 | 78.00 | 67.03 | 63.60 | 61.82 | 73.03 | 84.00 | 75.91 | 71.91 |
DenseNet169 | 79.00 | 68.10 | 51.97 | 76.35 | 72.81 | 78.47 | 67.84 | 70.65 |
DenseNet201 | 78.00 | 73.92 | 61.18 | 70.61 | 74.12 | 80.90 | 78.35 | 73.87 |
DINO(ResNet50) | 70.52 | 73.33 | 76.10 | 70.10 | 71.30 | 79.86 | 77.24 | 74.06 |
DINO(ViT) | 77.26 | 83.01 | 78.31 | 78.74 | 78.70 | 87.15 | 83.61 | 80.97 |
Tab. 4 Results of anomaly detection at second stage by different models
模型 | 准确率 | 平均准确率 | ||||||
---|---|---|---|---|---|---|---|---|
肩膀 | 肘部 | 手指 | 前臂 | 手部 | 肱骨 | 手腕 | ||
Inception | 79.00 | 82.97 | 60.32 | 65.61 | 88.16 | 80.38 | 71.09 | 75.36 |
VGG16 | 50.00 | 65.00 | 70.00 | 89.00 | 81.36 | 56.94 | 73.93 | 69.46 |
VGG19 | 50.00 | 60.00 | 75.00 | 87.00 | 80.70 | 56.94 | 84.00 | 70.52 |
Xception | 81.00 | 78.45 | 69.74 | 68.58 | 79.61 | 56.60 | 75.00 | 72.71 |
ResNet50 | 79.00 | 69.18 | 64.69 | 78.72 | 85.75 | 77.78 | 74.09 | 75.60 |
ResNet101 | 80.00 | 74.78 | 63.82 | 62.84 | 81.14 | 80.21 | 77.13 | 74.27 |
ResNet152 | 76.00 | 72.84 | 69.52 | 70.72 | 78.07 | 78.82 | 74.70 | 74.31 |
DenseNet121 | 78.00 | 67.03 | 63.60 | 61.82 | 73.03 | 84.00 | 75.91 | 71.91 |
DenseNet169 | 79.00 | 68.10 | 51.97 | 76.35 | 72.81 | 78.47 | 67.84 | 70.65 |
DenseNet201 | 78.00 | 73.92 | 61.18 | 70.61 | 74.12 | 80.90 | 78.35 | 73.87 |
DINO(ResNet50) | 70.52 | 73.33 | 76.10 | 70.10 | 71.30 | 79.86 | 77.24 | 74.06 |
DINO(ViT) | 77.26 | 83.01 | 78.31 | 78.74 | 78.70 | 87.15 | 83.61 | 80.97 |
部位 | DINO(ViT) | DINO(ResNet50) | ||
---|---|---|---|---|
灵敏度 | 特异度 | 灵敏度 | 特异度 | |
平均值 | 76.36 | 85.98 | 70.71 | 80.55 |
肩膀 | 74.92 | 80.24 | 67.66 | 74.78 |
肘部 | 81.85 | 82.01 | 68.94 | 80.81 |
手指 | 73.68 | 80.25 | 72.03 | 80.44 |
前臂 | 73.09 | 94.23 | 64.56 | 82.11 |
手部 | 78.03 | 78.71 | 68.93 | 80.65 |
肱骨 | 88.73 | 84.93 | 78.85 | 81.06 |
手腕 | 79.39 | 90.74 | 73.99 | 84.03 |
Tab. 5 Sensitivity and specificity of different pre-trained models under DINO framework
部位 | DINO(ViT) | DINO(ResNet50) | ||
---|---|---|---|---|
灵敏度 | 特异度 | 灵敏度 | 特异度 | |
平均值 | 76.36 | 85.98 | 70.71 | 80.55 |
肩膀 | 74.92 | 80.24 | 67.66 | 74.78 |
肘部 | 81.85 | 82.01 | 68.94 | 80.81 |
手指 | 73.68 | 80.25 | 72.03 | 80.44 |
前臂 | 73.09 | 94.23 | 64.56 | 82.11 |
手部 | 78.03 | 78.71 | 68.93 | 80.65 |
肱骨 | 88.73 | 84.93 | 78.85 | 81.06 |
手腕 | 79.39 | 90.74 | 73.99 | 84.03 |
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