查询结果:   吴能光,王华珍,许晓泓,刘俊龙,何霆.MLICP-CNN:基于CNN与ICP的多标记胸片置信诊断模型[J].计算机应用与软件,2019,36(7):177 - 182,191.
中文标题
MLICP-CNN:基于CNN与ICP的多标记胸片置信诊断模型
发表栏目
人工智能与识别
摘要点击数
863
英文标题
MLICP-CNN: MULTI-LABEL CHEST X-RAY CONFIDENCE DIAGNOSIS MODEL BASED ON CNN AND ICP
作 者
吴能光 王华珍 许晓泓 刘俊龙 何霆 Wu Nengguang Wang Huazhen Xu Xiaohong Liu Junlong He Ting
作者单位
华侨大学计算机科学与技术学院 福建 厦门 361021 厦门大学附属第一医院儿科 福建 厦门 361003    
英文单位
School of Computer Science and Technology, Huaqiao University, Xiamen 361021,Fujian,China Department of Pediatrics, The First Affiliated Hospital of Xiamen University, Xiamen 361003,Fujian,China    
关键词
多标记学习 归纳一致性预测器 卷积神经网络 X线胸片诊断 置信预测
Keywords
Multi-label learning Inductive conformal predictor Convolutional neural network Chest X-ray diagnosisConfidence prediction
基金项目
国家自然科学基金面上项目(61673186);福建省自然科学基金面上项目(2012J01274)
作者资料
吴能光,硕士生,主研领域:智慧医疗,图像处理。王华珍,副教授。许晓泓,硕士生。刘骏龙,本科生。何霆,教授。吴谨准,主任医师。 。
文章摘要
针对胸片的多标记预测集缺少可校准性的缺陷,提出一种基于卷积神经网络(Convolutional Neural Networks,CNN)与归纳一致性预测器( Inductive Conformal Prediction,ICP)的多标记胸片置信诊断模型MLICP-CNN。该模型将学习数据划分为训练集和校准集,通过使用CNN从训练集中学习出规则D。基于规则D和校准集使用算法随机性对被测数据进行置信预测,即为每个被测数据提供附带置信度的多标记预测集。在对Chest X-ray14胸片数据集的实验结果表明, 该模型在临床常用的95%置信度下,模型准确率为95%,体现了置信度评估的恰好可校准性。在CNN架构为Resenet50并采用LS-MLICP为奇异值映射函数下,模型性能最好,其确定预测率为96.43%,理想预测率为92.31%。另外,CNN架构对预测效率的影响程度远远小于奇异值映射函数。
Abstract
To address the absence of calibrated confidence evaluation of multi-label prediction for chest x-ray, we proposed a multi-label chest X-ray confidence diagnosis model based on CNN and ICP, named MLICP-CNN. Our model divided the learning data into training set and calibration set, and a rule D was learned from the training set through CNN. Based on rule D and calibration set, we used the randomness of the algorithm to predict the confidence of the measured data, that is, to provide a multi-label prediction set with confidence for each measured data. The experimental results on the chest X-ray14 set demonstrate that the accuracy rate of MLICP-CNN is exactly 95% under the confidence levels of 95% in common clinical, revealing the exactly validity of confidence evaluation. In addition, when using Resenet50 as the component of CNN framework and adopting LS-MLICP as a nonconformity measure, our model gains the best performance with the certain prediction of 96.43% and favorite prediction of 92.31%. The influence of CNN framework on prediction efficiency is significantly less than that of the nonconformity measure.
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