文章将深度学习应用于颈动脉斑块超声图像识别,分析讨论了不同感兴趣区域(Region of Interest,ROI)选取方式对卷积神经网络识别斑块性能的影响,并通过迁移学习来训练卷积神经网络。实验结果表明,采用分割出血管内外膜的ROI作为训练集时...文章将深度学习应用于颈动脉斑块超声图像识别,分析讨论了不同感兴趣区域(Region of Interest,ROI)选取方式对卷积神经网络识别斑块性能的影响,并通过迁移学习来训练卷积神经网络。实验结果表明,采用分割出血管内外膜的ROI作为训练集时,网络的识别能力最好,受试者操作特性(Receiver Operating Characteristic,ROC)曲线下面积为0.972。另外,用分割出血管内外膜的ROI对网络进行预训练,之后再用原始ROI进行微调,也可以有效提高卷积神经网络对原始ROI的识别能力,ROC曲线下面积从0.802提高至0.856。展开更多
Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approach...Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approaches to image fusion which are closely related to the standard TV-L_(2) and TV-L_(1) image approximation methods.We investigate their convex optimization formulations,under the perspective of primal and dual,and propose their associated new image decomposition models.In addition,we consider the TV-L_(1) based image fusion approach and study the specified problem of fusing two discrete-constrained images f_(1)(x)∈L_(1) and f_(2)(x)∈L_(2),where L_(1) and L_(2) are the sets of linearly-ordered discrete values.We prove that the TV-L_(1) based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discretevalued set u(x)∈L_(1)∪L_(2).This extends the results for the global optimization of the discrete-constrained TV-L_(1) image approximation[8,36]to the case of image fusion.As a big numerical advantage of the two proposed dual models,we show both of them directly lead to new fast and reliable algorithms,based on modern convex optimization techniques.Experiments with medical images,remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion.We also apply the new variational approaches to fusing 3D medical images.展开更多
文摘文章将深度学习应用于颈动脉斑块超声图像识别,分析讨论了不同感兴趣区域(Region of Interest,ROI)选取方式对卷积神经网络识别斑块性能的影响,并通过迁移学习来训练卷积神经网络。实验结果表明,采用分割出血管内外膜的ROI作为训练集时,网络的识别能力最好,受试者操作特性(Receiver Operating Characteristic,ROC)曲线下面积为0.972。另外,用分割出血管内外膜的ROI对网络进行预训练,之后再用原始ROI进行微调,也可以有效提高卷积神经网络对原始ROI的识别能力,ROC曲线下面积从0.802提高至0.856。
基金J.Yuan and A.Fenster gratefully acknowledge funding from the Canadian Institutes of Health Research,and the Ontario Institute of Cancer ResearchB.Miles gratefully acknowledges funding from the Graduate Program in BioMedical Engineering at the University of Western Ontario and the Computer Assisted Medical Intervention Training Program,which is funded by the Natural Sciences and Engineer-ing Research Council of Canada.A.Fenster holds a Canada Research Chair in Biomedi-cal Engineering,and acknowledges the support of the Canada Research Chair Program.
文摘Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approaches to image fusion which are closely related to the standard TV-L_(2) and TV-L_(1) image approximation methods.We investigate their convex optimization formulations,under the perspective of primal and dual,and propose their associated new image decomposition models.In addition,we consider the TV-L_(1) based image fusion approach and study the specified problem of fusing two discrete-constrained images f_(1)(x)∈L_(1) and f_(2)(x)∈L_(2),where L_(1) and L_(2) are the sets of linearly-ordered discrete values.We prove that the TV-L_(1) based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discretevalued set u(x)∈L_(1)∪L_(2).This extends the results for the global optimization of the discrete-constrained TV-L_(1) image approximation[8,36]to the case of image fusion.As a big numerical advantage of the two proposed dual models,we show both of them directly lead to new fast and reliable algorithms,based on modern convex optimization techniques.Experiments with medical images,remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion.We also apply the new variational approaches to fusing 3D medical images.
