In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(...目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(T_(2)WI、ADC和DCE序列),并根据最新的BIRADS提取特征,使用ROI之间的类内系数(ICC)来评估读者间协议。结果研究人群分为:luminal A 89例(26%),luminal B HER2阳性39例(11.5%),luminal B HER2阴性168例(48.5%),三阴性(TNBC)41例(12%),HER2富集7例(2%)。Luminal内A肿瘤与特殊的组织学类型、最小的肿瘤大小和持续的动力学曲线相关(P均<0.05)。Luminal B HER2阴性肿瘤与最低ADC值相关(0.77×10^(-3)mm^(2)/s^(2)),其预测BC分子亚型的准确性为0.583。TNBC与不对称和中度/显著BPE,圆形/椭圆形肿块,边缘受限和边缘增强相关(P均<0.05)。HER2富集的BC与最大肿瘤大小相关(平均37.28mm,p值=0.02)。结论BC分子亚型与T_(2)WI、ADC和DCE MRI特征相关,ADC有助于预测luminal B HER2阴性病例。展开更多
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
文摘目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(T_(2)WI、ADC和DCE序列),并根据最新的BIRADS提取特征,使用ROI之间的类内系数(ICC)来评估读者间协议。结果研究人群分为:luminal A 89例(26%),luminal B HER2阳性39例(11.5%),luminal B HER2阴性168例(48.5%),三阴性(TNBC)41例(12%),HER2富集7例(2%)。Luminal内A肿瘤与特殊的组织学类型、最小的肿瘤大小和持续的动力学曲线相关(P均<0.05)。Luminal B HER2阴性肿瘤与最低ADC值相关(0.77×10^(-3)mm^(2)/s^(2)),其预测BC分子亚型的准确性为0.583。TNBC与不对称和中度/显著BPE,圆形/椭圆形肿块,边缘受限和边缘增强相关(P均<0.05)。HER2富集的BC与最大肿瘤大小相关(平均37.28mm,p值=0.02)。结论BC分子亚型与T_(2)WI、ADC和DCE MRI特征相关,ADC有助于预测luminal B HER2阴性病例。
