乳腺图像的感兴趣区域(region of interest,ROI)检测是计算机辅助诊断乳腺疾病的第一步,检测效果的提升对减小误诊率有重要的作用.传统方法往往提取单独的视觉特征来描述乳腺图像,通过分类的方法找出包含肿块的区域.然而由于乳腺图像内...
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乳腺图像的感兴趣区域(region of interest,ROI)检测是计算机辅助诊断乳腺疾病的第一步,检测效果的提升对减小误诊率有重要的作用.传统方法往往提取单独的视觉特征来描述乳腺图像,通过分类的方法找出包含肿块的区域.然而由于乳腺图像内容丰富结构复杂,使用单一的底层视觉容易忽视特征间的相互联系.提出基于相关性特征融合的乳腺图像ROI检测框架(multi-cue integration detection,MCID),通过引入乳腺图像的相关性特征,并与乳腺图像局部视觉特征相融合,辅助乳腺图像ROI的检测,以提高检测准确性.乳腺图像ROI检测实验表明,MCID可提高肿块检测的准确性.
In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorith...
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In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is ***, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction ***, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise ***, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.
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