Architectures of deep neural networks
This protocol is extracted from research article:
Fully end-to-end deep-learning-based diagnosis of pancreatic tumors
Theranostics, Jan 1, 2021; DOI: 10.7150/thno.52508

Figure Figure44 shows the detailed architectures of the three sub-networks (see also Table S1-S3). ResNet18 and ResNet34 have similar architectures but different depths, as shown in Figures Figures44A and 4C. The convolutional layers mostly capture the main local features of images with 3 × 3 filters, and the last fully connected layer gives a binary classification according to the global feature connected from all local features. Unlike a plain convolutional neural network (CNN), ResNet avoids gradient vanishing by using identity and down-sampling blocks. The former keeps the shape of the input and output the same, while the latter halves the size of the output and doubles the number of channels. By adding direct paths, information of the input or gradient is allowed to pass through multiple layers to improve accuracy. U-Net32 consists of four down-sampling and four up-sampling steps, which reduce the 512 × 512 × 1 input image to a 32 × 32 × 256 representation, which is then up-sampled to a 512 × 512 × 2 output. During down sampling, each step contains a convolution block, followed by a max pooling layer and a dropout layer. Up sampling has each step consisting of a transpose convolution layer, followed by a dropout layer and a convolution block. A key feature of the U-Net architecture is that the convolutional kernel output from the encoding half of the network is concatenated with each corresponding decoding step, which helps preserve the details of the original image. The final layer consists of a convolution with two 1 × 1 kernels, which outputs a score for each of two classes: belonging to the pancreas or not. The final segmentation is achieved by selecting the class with the highest score for each pixel. We accelerated the training process by using z-score normalization and batch normalization layers in all sub-networks. At the same time, a dropout layer and L2 regularization are used to prevent overfitting.

Architectures of the three sub-networks: (A) ResNet18 for pancreas location, (B) U-Net32 for pancreas segmentation, and (C) ResNet34 for pancreatic tumor diagnosis. (D) Detailed structures of the identity (ID), down sampling (DS), and convolution (Conv) blocks. (AvgPool, average-pooling; BN, batch normalization; Concate, concatenation; FC, fully connected; MaxPool, max-pooling; ReLU, rectified linear unit; Trans, transposed).

All training, validation, and testing processes were performed in TensorFlow on a NVIDIA GeForce GTX 1050 Ti GPU. Adam optimizer 19 was used with default parameters β1 = 0.9 and β2 = 0.999. The dropout rate of the dropout layer was 0.4. Loss was calculated according to cross entropy. ResNet18's batch size was set to 32, and the epoch to 100. Learning rate was initialized at 1 × 10-3, and reduced by a factor of 10 every 20 epochs. U-Net32 had a batch size of 2 and epoch of 50. Learning rate was initialized at 5 × 10-4, and reduced by a factor of 20 every 20 epochs. ResNet34 had a batch size of 32, epoch of 86, and learning rate of 5 × 10-5.

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