目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性...目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。展开更多
利用目标辐射源空间分布的稀疏性,提出了一种基于稀疏表示的多快拍联合波达方向(direction of arrival,DOA)估计方法。该方法首先利用采样数据矩阵大奇异值对应的左奇异向量估计信号子空间,然后采用加权迭代最小方差方法对信号空间进行...利用目标辐射源空间分布的稀疏性,提出了一种基于稀疏表示的多快拍联合波达方向(direction of arrival,DOA)估计方法。该方法首先利用采样数据矩阵大奇异值对应的左奇异向量估计信号子空间,然后采用加权迭代最小方差方法对信号空间进行稀疏表示。与传统的角度高分辨估计方法不同,该方法没有利用样本的统计信息,因而对具有任意相关性的信号源能进行有效的波达方向估计,不需要进行去相关处理,且具有很高的分辨力及估计精度。实验表明在该方法能准确的对目标源方位进行估计,且极大地降低了稀疏表示的计算量。展开更多
Millimeter-wave communication （mmWC） is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional anten...Millimeter-wave communication （mmWC） is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station （BS） and mobile sets （MS）. Unlike the conventional MIMO systems, Millimeter-wave （mmW） systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding （combining） architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector （MMV） greedy sparse framework and subspace method of Multiple Signal Classification （MUSIC） which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.展开更多
文摘目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。
文摘利用目标辐射源空间分布的稀疏性,提出了一种基于稀疏表示的多快拍联合波达方向(direction of arrival,DOA)估计方法。该方法首先利用采样数据矩阵大奇异值对应的左奇异向量估计信号子空间,然后采用加权迭代最小方差方法对信号空间进行稀疏表示。与传统的角度高分辨估计方法不同,该方法没有利用样本的统计信息,因而对具有任意相关性的信号源能进行有效的波达方向估计,不需要进行去相关处理,且具有很高的分辨力及估计精度。实验表明在该方法能准确的对目标源方位进行估计,且极大地降低了稀疏表示的计算量。
文摘Millimeter-wave communication （mmWC） is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station （BS） and mobile sets （MS）. Unlike the conventional MIMO systems, Millimeter-wave （mmW） systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding （combining） architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector （MMV） greedy sparse framework and subspace method of Multiple Signal Classification （MUSIC） which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.
