Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward *** this article,we review the recent progress of AI-enabled medical ***,we briefly review the background about AI i...
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Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward *** this article,we review the recent progress of AI-enabled medical ***,we briefly review the background about AI in its way of ***,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and ***,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and ***,we report the way of human-machine *** believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.
关于时间序列分类的问题在过去十多年时间里已经引起极大的兴趣.并且已经有实验表明传统流行的分类算法像KNN等,已经很难处理时间序列的分类问题.基于Shapelet和DTW(动态时间规整,Dynamic Time Warping)的这一分类方法的时间复杂度又太...
详细信息
关于时间序列分类的问题在过去十多年时间里已经引起极大的兴趣.并且已经有实验表明传统流行的分类算法像KNN等,已经很难处理时间序列的分类问题.基于Shapelet和DTW(动态时间规整,Dynamic Time Warping)的这一分类方法的时间复杂度又太高.本文提出一种新的基于子段距离计算的时序分类方法,通过对时间序列进行切分然后对切分后的子段用k-shape算法进行聚类,在聚类结果中寻找两类时间序列各自比较有区分性的片段,并以此来作为分类的依据,该方法思路更为简单且时间复杂度不高.通过实验验证了我们算法的分类精度和适用性,并与shaplet算法相比我们算法在时间复杂度上更具优势.
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