Transfer learning25,26 is a method where previously learned knowledge is applied to another task and the task domains may be different. In our case, the domain is very differ- ent. ImageNet consists of natural camera images and does not include any type of lung nodule or cancer image. Our image set consists of only lung nodules in CT images. We experimented with three different pretrained CNN’s [vgg (visual geometry group)-m/vgg-f/vgg-s]27 in this study using a MA TLAB toolbox named MA TCONVNET.28 The f, m, and s after vgg- stand for fast, medium, and slow and refer to training time (so partly the number of weights). We obtained deep features from the outputs of the last fully connected layer after applying the activation function using a rectified linear unit (post-ReLU), which changes all values <0 to be 0. The LDCT images were grayscale (no color component and we changed the voxel intensities of LDCT images to 0-255 or 8 bit), but the pretrained network was trained on RGB images, so we normalized the images by the average red, green, and blue channel images, and exper- imented by using each channel separately. 解释

Transfer learning是一种方法，其中先前学习的知识被应用于另一个任务，任务域可能不同。在我们的情况下，领域非常不同。ImageNet由自然相机图像组成，不包括任何类型的肺结节或癌症图像。我们的图像集仅包含CT图像中的肺结节。在本研究中，我们使用名为MATCONVNET的MATLAB工具箱尝试了三种不同的预训练CNN（vgg（visual geometry group）-m / vgg-f / vgg-s）进行实验。vgg之后的f，m和s代表快速，中等和慢，并且指的是训练时间（因此部分权重的数量）。我们从应用激活函数后的最后一个完全连接层的输出中获得深层特征，使用修正线性单元（post-ReLU）对所有值<0进行更改以使其为0。LDCT图像是灰度的（没有彩色分量），我们将LDCT图像的体素强度更改为0-255或8位，但预训练网络是在RGB图像上训练的，因此我们通过平均红色，绿色和蓝色通道图像对图像进行归一化，并尝试分别使用每个通道。