Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into ...Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into their decision-making processes.This limitation underscores the need for advanced diagnostic tools that enhance both accuracy and interpretability.This study aims to integrate cutting-edge deep learning algorithms with Gradient-weighted Class Activation Mapping(Grad-CAM)to improve the accuracy and transparency of breast cancer diagnostics through mammographic analysis.We proposed robust approaches using MobileNet,Xception,and DenseNet models,enhanced with Grad-CAM,to analyze mammogram images.This integration facilitates a deeper understanding of model decisions,highlighting critical diagnostic features through visual explanations.The models were rigorously tested on the MIAS dataset to evaluate their diagnostic performance and reliability,achieving a diagnostic accuracy of 94.17%,demonstrating superior performance compared to traditional methods.The findings show significant potential for clinical application,promising to enhance patient outcomes through more accurate and transparent diagnostic practices in oncology.展开更多
频谱兼容波形利用多段离散寂静带宽合成大带宽,在满足带宽要求的同时有效对抗频域密集干扰。为了抑制频谱兼容波形的峰值旁瓣水平,提出一种低峰值旁瓣频谱兼容波形设计方案。所提方案综合考虑波形的自相关峰值旁瓣性能和抗干扰性能,构...频谱兼容波形利用多段离散寂静带宽合成大带宽,在满足带宽要求的同时有效对抗频域密集干扰。为了抑制频谱兼容波形的峰值旁瓣水平,提出一种低峰值旁瓣频谱兼容波形设计方案。所提方案综合考虑波形的自相关峰值旁瓣性能和抗干扰性能,构建加权目标函数。在波形恒模约束下,该问题为非确定多项式难(non-deterministic polynomial-hard,NP-hard)问题。为此,首先利用指数对数平滑技术逼近目标函数,进而提出基于快速傅里叶变换的共轭梯度(conjugate gradient method based on fast Fourier transformation,CGFFT)法求解该问题。此外,波形设计中需要根据性能指标要求选择合适的加权值,为此提出一种加权值自适应确定方法,最后通过数值仿真验证了所提方法的有效性。展开更多
目的旨在构建一套具备可解释性与置信度分析功能的前列腺癌(orostate cancer,PCa)良恶性分类模型,以提升诊断准确性并降低临床误诊风险。方法回顾性分析267例PCa患者和143例非PCa患者的双参数磁共振数据,采用VGG-16网络构建分类模型,通...目的旨在构建一套具备可解释性与置信度分析功能的前列腺癌(orostate cancer,PCa)良恶性分类模型,以提升诊断准确性并降低临床误诊风险。方法回顾性分析267例PCa患者和143例非PCa患者的双参数磁共振数据,采用VGG-16网络构建分类模型,通过梯度加权类激活映射(gradient-weighted class activation mapping,Grad-CAM)方法实现可视化解释,并使用蒙特卡洛Dropout(Monte Carlo Dropout,MC-Dropout)法进行不确定性估计,引入拒绝机制;最后通过受试者工作特性(receiver operating characteristic,ROC)曲线和曲线下面积(area under the curve,AUC)评估模型性能。结果相比于原始VGG-16网络,本次提出的置信度模型提高了正确分类比例(94.6%vs 79.3%),并减少了假阳性(5.3%vs 15.3%),同时漏诊率接近零(0.1%),AUC值提高(P<0.05);模型正确分类比例高于高年资医生(94.6%vs 90.8%),高置信度激活区域与真实病灶区域高度吻合。结论本次提出的PCa分类模型,结合可视化与拒绝机制,无需像素级标签,也可准确识别PCa病灶并输出置信度,显著提高临床决策的准确性与安全性。展开更多
基金supported by the National Key R&D Program of the China Project No.2020YFB2104402.
文摘Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into their decision-making processes.This limitation underscores the need for advanced diagnostic tools that enhance both accuracy and interpretability.This study aims to integrate cutting-edge deep learning algorithms with Gradient-weighted Class Activation Mapping(Grad-CAM)to improve the accuracy and transparency of breast cancer diagnostics through mammographic analysis.We proposed robust approaches using MobileNet,Xception,and DenseNet models,enhanced with Grad-CAM,to analyze mammogram images.This integration facilitates a deeper understanding of model decisions,highlighting critical diagnostic features through visual explanations.The models were rigorously tested on the MIAS dataset to evaluate their diagnostic performance and reliability,achieving a diagnostic accuracy of 94.17%,demonstrating superior performance compared to traditional methods.The findings show significant potential for clinical application,promising to enhance patient outcomes through more accurate and transparent diagnostic practices in oncology.
文摘频谱兼容波形利用多段离散寂静带宽合成大带宽,在满足带宽要求的同时有效对抗频域密集干扰。为了抑制频谱兼容波形的峰值旁瓣水平,提出一种低峰值旁瓣频谱兼容波形设计方案。所提方案综合考虑波形的自相关峰值旁瓣性能和抗干扰性能,构建加权目标函数。在波形恒模约束下,该问题为非确定多项式难(non-deterministic polynomial-hard,NP-hard)问题。为此,首先利用指数对数平滑技术逼近目标函数,进而提出基于快速傅里叶变换的共轭梯度(conjugate gradient method based on fast Fourier transformation,CGFFT)法求解该问题。此外,波形设计中需要根据性能指标要求选择合适的加权值,为此提出一种加权值自适应确定方法,最后通过数值仿真验证了所提方法的有效性。
文摘目的旨在构建一套具备可解释性与置信度分析功能的前列腺癌(orostate cancer,PCa)良恶性分类模型,以提升诊断准确性并降低临床误诊风险。方法回顾性分析267例PCa患者和143例非PCa患者的双参数磁共振数据,采用VGG-16网络构建分类模型,通过梯度加权类激活映射(gradient-weighted class activation mapping,Grad-CAM)方法实现可视化解释,并使用蒙特卡洛Dropout(Monte Carlo Dropout,MC-Dropout)法进行不确定性估计,引入拒绝机制;最后通过受试者工作特性(receiver operating characteristic,ROC)曲线和曲线下面积(area under the curve,AUC)评估模型性能。结果相比于原始VGG-16网络,本次提出的置信度模型提高了正确分类比例(94.6%vs 79.3%),并减少了假阳性(5.3%vs 15.3%),同时漏诊率接近零(0.1%),AUC值提高(P<0.05);模型正确分类比例高于高年资医生(94.6%vs 90.8%),高置信度激活区域与真实病灶区域高度吻合。结论本次提出的PCa分类模型,结合可视化与拒绝机制,无需像素级标签,也可准确识别PCa病灶并输出置信度,显著提高临床决策的准确性与安全性。
