System Model and Access Control Schemes for Medical Image Collaborative Analysis

doi:10.3778/j.issn.1673-9418.2101091

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1779-1791.DOI: 10.3778/j.issn.1673-9418.2101091

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System Model and Access Control Schemes for Medical Image Collaborative Analysis

LIU Tonglai¹, ZHANG Zikai², WU Jigang

1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2021-01-20 Revised:2021-03-18 Online:2022-08-01 Published:2021-03-26
About author:LIU Tonglai, born in 1982, Ph.D. candidate, member of CCF. His research interests include blockchain, intelligent computing and data mining.
ZHANG Zikai, born in 1989, Ph.D. candidate. His research interests include data security, network security and machine learning.
WU Jigang, born in 1963, Ph.D., professor, Ph.D. supervisor, member of CCF. His research interests include intelligent computing and mobile computing.
Supported by:
the National Natural Science Foundation of China(62072118);the Natural Science Foundation of Guangdong Province(2018B030311007)

面向医疗图像协同分析的系统模型及访问控制

刘同来¹, 章子凯², 武继刚

1. 广东工业大学计算机学院，广州 510006
2. 北京交通大学电子信息工程学院，北京 100044

作者简介:刘同来（1982—），男，江苏连云港人，博士研究生，CCF会员，主要研究方向为区块链、智能计算、数据挖掘。
章子凯（1989—），男，河南周口人，博士研究生，主要研究方向为数据安全、网络安全、机器学习。
武继刚（1963—），男，江苏徐州人，博士，教授，博士生导师，CCF会员，主要研究方向为智能计算、移动计算。
基金资助:
国家自然科学基金(62072118);广东省自然科学基金(2018B030311007)

Abstract

Abstract:

Deep learning based medical image analysis has played an important role in the computer-aided diagnosis and treatment for diseases. The accuracy of classification has always been the primary goal pursued by researchers. However, the transmission process of images also faces the problems of limited bandwidth in WAN and increased risks of data security. Additionally, individual privacy is vulnerable when user data are exposed to an unauthorized user. To address these problems, this paper constructs a system model for collaborative analysis of diagnosis of diabetic retinopathy (DR). This model consists of two stages: data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the trained model into the blockchain, other private clouds can use the best-performing model shared by private clouds on the blockchain to identify the image quality and transfer high-quality image to the lesion classification model for use. In the classification stage of lesions, each private cloud uses high-quality images for classification and uploads its model parameters to the public cloud for aggregation to obtain a global model. Then, the public cloud sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transferred, and protect personal privacy. The access control scheme includes the improved role-based access control (RAC) used within the private cloud and the blockchain-based access control scheme (BAC) used during the interaction between the private cloud and the public cloud. RAC can grant both functional and data access permissions to roles, and consider object attributes to realize fine-grained control. BAC is based on certificateless public key cryptography technology and blockchain technology, which can realize identity authentication and permission identification of private cloud while requesting to transfer model parameters from private cloud to public cloud, protect the identity, permission and model parameters of private cloud, and achieve lightweight access control. Two retinal datasets are utilized for the classification of DR. Experimental results demonstrate that data cleaning can efficiently remove low quality images and improve the accuracy of the classifica-tion for early lesions of DR. The accuracy is up to 90.2%.

Key words: medical image, collaborative analysis, access control, data security, blockchain

摘要：

基于深度学习的医疗图像分析技术在计算机辅助的疾病诊断和治疗中发挥了重要作用。分类准确性一直是科研工作者追求的首要目标。然而，图像传输过程还会面临广域网带宽有限及不安全隐患增大的问题。并且当用户数据暴露给未经授权的用户时，平台很容易泄漏个人隐私。针对上述问题，构建了面向糖尿病视网膜病变（DR）诊断协同分析的系统模型及访问控制方案。系统模型包括数据清洗和病变分类两个阶段。在数据清洗阶段，私有云将训练后得到的模型写入区块链，其他私有云清洗数据时使用链上性能最好的模型来识别图像质量，并把高质量图像传递给病变分类模型使用。在病变分类阶段，各私有云分别训练分类模型，并将自己的模型参数上传至公有云聚合得到全局模型，然后将全局模型下发给各私有云，实现协同学习，降低数据传输量，并保护个人隐私。访问控制方案包括私有云内部使用改进的基于角色的访问控制（RAC）和私有云与公有云交互过程中使用的基于区块链的访问控制方案（BAC）。RAC可同时给角色授予功能权限和数据访问权限，并考虑对象属性，实现细粒度级别控制。BAC基于无证书公钥加密技术和区块链技术，能够在私有云向公有云请求传输模型参数的同时，实现对私有云的身份认证、权限识别，保护私有云身份、权限和模型参数的安全，达到轻量级访问控制效果。使用两个视网膜数据集来做DR的分类分析，实验结果表明，数据清洗能够有效地去除低质量图像，提高医生早期病变分类的准确性，准确率达到90.2%。

关键词: 医疗图像, 协同分析, 访问控制, 数据安全, 区块链

CLC Number:

TP393

LIU Tonglai, ZHANG Zikai, WU Jigang. System Model and Access Control Schemes for Medical Image Collaborative Analysis[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1779-1791.

刘同来, 章子凯, 武继刚. 面向医疗图像协同分析的系统模型及访问控制[J]. 计算机科学与探索, 2022, 16(8): 1779-1791.

Figures/Tables 12

Fig.1 System model for medical image collaborative analysis

Fig.2 Architecture for data cleaning

Fig.3 Architecture for lesion classification

Table 1 Model parameters of data cleaning

层	类型	特征图大小	尺寸	步长	参数数量
0	Input	88×88×3	—	—	—
1	Conv	88×88×24	5×5×24	1	624
2	Conv	80×80×16	3×3×16	1	160
3	Conv	78×78×8	3×3×8	1	80
4	Max-pooling	39×39×8	2×2	2	—
5	FC	64	—	—	778 688
6	FC	2	—	—	126

Fig.4 Model of data cleaning

Table 2 Level of data access

级别	编码
个人级别	00
组级别	01
部门级别	10
医院级别	11

Table 3 Control policy

角色	对象	操作权限	数据权限	基于属性的动态数据权限
$r 1$	$o 1$	1010	01	score≥90 and lesion=HA
	$o 2$	0100	00	—
	$o 3$	0000	00	—
$r 2$	$o 1$	1110	10	—
	$o 2$	1010	00	—
	$o 3$	0000	00	—

Table 3 Control policy

角色	对象	操作权限	数据权限	基于属性的动态数据权限
$r 1$	$o 1$	1010	01	score≥90 and lesion=HA
	$o 2$	0100	00	—
	$o 3$	0000	00	—
$r 2$	$o 1$	1110	10	—
	$o 2$	1010	00	—
	$o 3$	0000	00	—

Table 4 Comparison of role-based access control

模型	RAC	ABCCC^[14]	E-RBAC^[26]	SAT-RBAC^[27]	ABAC-IaaS^[13]
支持属性	支持	支持	不支持	不支持	支持
灵活性	高	低	高	低	一般
动态性	高	低	低	低	高
扩展性	高	低	低	低	高
多层级安全控制	支持	支持	不支持	不支持	不支持
模型安全	高	高	低	一般	低

Fig.5 Comparison of running time

Fig.6 Classification results of HA and HEs on DB1 dataset

Fig.7 FROC curves for DR lesions classification

Table 5 Sensitivity of DR lesions classification

应用场景	出血点	硬性渗出物	迭代次数
未进行数据清洗	0.850	0.960	1 000
进行数据清洗	0.910	0.980	1 000
文献[20]	0.626	0.876	—
文献[35]	—	0.830	—
文献[36]	—	0.820	—
文献[37]	0.840	—	—

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System Model and Access Control Schemes for Medical Image Collaborative Analysis

面向医疗图像协同分析的系统模型及访问控制

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