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基于区块链的医疗影像数据人工智能检测模型
网络安全与数据治理 4期
陈思源1,2,谭艾迪3,魏双剑3,盖珂珂2,4
(1.北京理工大学 计算机学院,北京100081;2.北京理工大学长三角研究院(嘉兴),浙江 嘉兴314019; 3.中国船舶工业综合技术经济研究院,北京100081;4.北京理工大学 网络空间安全学院,北京100081)
摘要: 基于深度学习的目标检测技术被广泛应用于医疗检测领域,该技术依赖大量医疗影像训练分类模型,从而为医生决策提供有力的辅助医疗手段。因涉及患者隐私并直接关系到医生诊断,所以医疗影像数据的共享必须保护患者隐私并确保数据准确不被篡改,而现有中心化的医疗数据存储方案面临隐私泄露等诸多安全问题。提出了一种基于区块链的医疗影像数据人工智能检测模型。该模型针对目标检测技术辅助医生诊断的问题,采用区块链技术实现去中心化、不可篡改的训练参数聚合,通过加密和签名技术保护数据隐私,利用智能合约评估服务器诊断准确率,有助于解决医疗数据壁垒和医疗隐私泄露问题。
中圖分類號: TP311
文獻標識碼: A
DOI: 10.19358/j.issn.2097-1788.2022.04.003
引用格式: 陳思源,譚艾迪,魏雙劍,等. 基于區(qū)塊鏈的醫(yī)療影像數(shù)據(jù)人工智能檢測模型[J].網(wǎng)絡(luò)安全與數(shù)據(jù)治理,2022,41(4):21-25.
Blockchain-based artificial intelligence detection model for medical data
Chen Siyuan1,2,Tan Aidi3,Wei Shuangjian3,Gai Keke2,4
(1.School of Computer Science,Beijing Institute of Technology,Beijing 100081,China; 2.Yangtze Delta Region Academy of Beijing Institute of Technology,Jiaxing 314019,China; 3.China Institute of Marine Technology and Economy,Beijing 100081,China; 4.School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China)
Abstract: Deep learning-based target detection technology is being widely used in the field of medical detections. For training a large number of medical images, we can construct an effective classification model to effectively predict the disease situation of patients and provide a powerful auxiliary medical means of decision-making. In order to improve the prediction accuracy, massive training data are the premise to construct an effective learning model. However, medical data involve patients′ privacy and are directly related to diagnoses. Sharing medical data needs to guarantee privacy, accuracy and tamper-proof. Existing centralized medical storage schemes face many security issues, e.g., privacy disclosure. This paper proposes a blockchain-based artificial intelligence detection model for medical data that uses a target detection technology to assist physicians during the diagnosis process. In our model, blockchain technology supports realizing the decentralized and un-tampered aggregation of training parameters. Encryption and signature technology are used to protect privacy and smart Contract is implemented to evaluate the accuracy of server diagnosis. The proposed model will contribute to solving the issues in medical data barriers and privacy disclosure.
Key words : deep learning;blockchain;secure data sharing;artificial intelligence detection

0 引言

醫(yī)院每天產(chǎn)生和診斷大量的醫(yī)療影像,據(jù)統(tǒng)計在醫(yī)療數(shù)據(jù)中,影像數(shù)據(jù)占數(shù)據(jù)總量的90%以上。隨著醫(yī)療檢測設(shè)備的更新?lián)Q代和不斷增加,影像數(shù)據(jù)以每年超過30%的增長速度急劇增加。與此形成鮮明對比的是,醫(yī)生數(shù)量緩慢增長,這使得影像診斷如閱讀分析CT(計算機斷層掃描)等工作對醫(yī)生造成的負擔日益加劇,經(jīng)驗缺乏與工作量增大容易造成誤診。隨著大數(shù)據(jù)和人工智能技術(shù)的發(fā)展,利用計算機輔助診斷,使用基于人工智能的目標檢測技術(shù)幫助醫(yī)生做出快速判斷,對減輕醫(yī)生負擔、增加診斷準確率、提高就診效率而言就顯得十分必要且具有現(xiàn)實意義。

目標檢測技術(shù)因其廣泛的現(xiàn)實應(yīng)運用場景備受學(xué)術(shù)界和工業(yè)界關(guān)注。隨著計算機算力的不斷提升,目標檢測技術(shù)蓬勃發(fā)展,衍化出雙階段和單階段兩大類。




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作者信息:

陳思源1,2,譚艾迪3,魏雙劍3,蓋珂珂2,4

(1.北京理工大學(xué) 計算機學(xué)院,北京100081;2.北京理工大學(xué)長三角研究院(嘉興),浙江 嘉興314019;

3.中國船舶工業(yè)綜合技術(shù)經(jīng)濟研究院,北京100081;4.北京理工大學(xué) 網(wǎng)絡(luò)空間安全學(xué)院,北京100081)



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