DLR. A total number of 512 deep features are extracted (256 from PET images and 256 from CT images) as a byproduct of a multimodal neural network. The network is trained on CT and PET simultaneously [39], with two identical and parallel convolutional branches merged in a fully connected layer (see Fig. 4 for the details on the architecture). An internal transfer learning procedure is applied by first training the whole network on the T-stage dataset, then predicting LR by fine-tuning, i.e. retraining only the linear blocks (final blue box in Fig. 4). Fixed hyper-parameters are used to regulate the training process with Adam [43] optimizer (batch size: 32, epochs: 500, learning rate 10−3). Data augmentation procedures were used to improve the performance and reduce overfitting: i.e., minimal rotations, translations and Gaussian noise. The transformed images were resized to cubes of 64 × 64 × 64 to better fit the GPU memory size. 解释
时间: 2024-02-14 22:36:11 浏览: 29
这段文字描述了一个多模态神经网络,该网络从 PET(正电子发射断层扫描)和 CT(计算机断层扫描)图像中提取512个深度特征(256个来自PET图像,256个来自CT图像)。网络采用两个相同的卷积分支,然后将它们合并成一个全连接层。该网络首先在T-stage数据集上进行训练,然后通过微调(只重新训练线性块)来预测LR。训练过程中使用了固定的超参数来调节,使用Adam优化器,批大小为32,训练时代数为500,学习率为10^-3。数据增强过程被用来提高性能和减少过拟合:最小旋转、平移和高斯噪声等。转换后的图像被调整为64×64×64的立方体以更好地适应GPU内存大小。
相关问题
To overcome the shortcomings of radiomics methods, we developed a more advanced method, called deep learning-based radiomics (DLR). DLR obtains radiomics features by normalizing the information from a deep neural network designed for image segmentation. The main assumption of DLR is that once the image has been segmented accurately by the deep neural network, all the information about the segmented region will have already been installed in the network. Unlike current radiomics methods, in DLR, the high-throughput image features are directly extracted from the deep neural network. Because DLR does not involve extra feature extrac- tion operations, no extra errors will be introduced into the radiomics analysis because of feature calculations. The effectiveness of features is related only to the quality of segmentation. If the tumor has been segmented precisely, the accuracy and effectiveness of the image features can be guaranteed 解释
这段话提到了一个新的医学影像分析方法,叫做基于深度学习的放射组学(DLR)。与现有的放射组学方法不同,DLR直接从深度神经网络中提取高通量的图像特征,而不需要进行额外的特征提取操作,从而避免了因特征计算而引入额外的误差。DLR的主要假设是,一旦图像被深度神经网络准确地分割出来,所有与分割区域相关的信息都已经被嵌入到网络中。因此,DLR的特征有效性与分割的质量密切相关。如果肿瘤被准确地分割了,那么图像特征的准确性和有效性就能得到保证。
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*104 were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images. 解释
这段文本介绍了一种基于深度学习的影像分析方法,即Deep learning-based radiomics (DLR)。该方法使用了修改后的卷积神经网络(CNN)结构,通过对磁共振(MR)影像进行多模态分析,提取更深层次的信息。该研究使用了151例低级别胶质瘤患者的数据集,通过对肿瘤进行分割,得到CNN的最后卷积层信息,再利用Fisher向量进行编码,得到维度高达1.6*104的高通量特征。该研究表明,相比于传统的放射学方法,DLR方法在预测异柠酸脱氢酶1(IDH1)突变状态时,具有更高的准确性,其AUC值达到了92%~95%。这些发现表明,DLR方法可以有效地从医学影像中提取更深层次的信息,对肿瘤的诊断和治疗具有潜在的帮助。