# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 2 Methods  
### Deep Learning Model Architectures  
Multi-layer perceptron (MLP) has the most basic architecture of deep neural networks, which is composed a stack of processing layers: an input layer, several hidden layers, and an output layer (Figure 1) [18]. The neurons in the processing layers allow nonlinear computation and empower the model to learn different representations of the training data at multiple levels of abstraction [3]. Convolutional neural network (CNN) is the most widely applied deep learning algorithm for medical imaging studies. A typical CNN consists of convolutional layers with  
Figure 1: Architecture of multi-layer perceptron (MLP) [18]. activation functions, pooling layers, and fully connected layers (Figure 2) [2]. Convolutional layers convolve an image with different types of kernel functions to extract image features. The kernels are applied to the entire image, thus greatly reducing the number of weights to be trained compared to fully connected neural networks. Activation functions such as sigmoid and ReLu (Rectified Linear Unit) serve as nonlinear feature detectors to introduce nonlinearities to CNN. Pooling layers reduce feature map resolution with translational invariance. The combination of convolutional and pooling layers enables CNN to learn spatial hierarchies among feature patterns. Fully connected layers function as a classifier or regressor to predict the desired outcomes [2]. The weight sharing and translational invariance properties facilitate CNN the efficient and precise power on image processing tasks. Depending on the input data dimensionality, 1D, 2D, and 3D convolutional kernels can be employed. Besides the basic stacking of convolutional layers, pooling layers and fully connected layers, models with complex architectures have been developed to further improve the performance of CNN. AlexNet was the first big CNN model which showed the great potential of CNN on image recognition tasks [19]. Inception blocks utilize convolution kernels of different sizes at the same level to optimize the accuracy and computation time of the model [20]. Residual connection from a previous layer to a later layer without extra parameters solves the vanishing gradients issues and thereby make the CNN model with many layers [21]. Dense blocks formed by many convolution operations and a final pooling and connecting the input and output of each convolution are proposed to train even deeper models [22]. Many other CNN models with different architectures have been proposed. A detailed summary can be found in the review paper by [PERSON] et al. [2]. U-net was proposed for semantic segmentation in 2015 and is still one of the most used CNN architectures for medical image segmentation. The typical U-net is composed of symmetrical encoder and decoder paths connected by skip connections (Figure 3) [23]. The model first performs a set of convolutions at the encoder side to extract features from the input data and then reconstructs the input image while including new information by transposed convolutions at the decoder side. Skip connections connect the encoder and decoder at each level. Complex architectures have also been applied to U-net to further improve its performance, for example, the Res-U-net and U-net with attention mechanism [24; 25]. Auto-encoder plays a pivotal role in unsupervised deep learning. Auto-encoder follows the encoder and decoder architecture (Figure 4). The encoder aims at learning a latent representation with low dimensionality which retains only the significant information while ignoring the noise. The decoder utilizes the latent representation to reconstruct the input data. Auto-encoder provides an effective approach for feature learning in recognition tasks with unlabeled data. Variational auto-encoders are applied as generative models which randomly generate new data that are similar to the input data [2]. Figure 2: Architecture of convolutional neural networks [2].