# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Machine Learning and Deep Learning
#### 3.1.2 Deep Learning Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Architectures of a CNN**} \\ \hline
**Name** & **Reference** & **Details** \\ \hline \multirow{2}{*}{LeNet} & Backpropagation Applied to & \multirow{2}{*}{[PERSON] 1990. Read zip codes, digits.} \\  & Handwritten Zip Code Recognition [129] & \\ \hline \multirow{2}{*}{AlexNet} & ImageNet Classification with Deep & [PERSON], [PERSON] and [PERSON], in 2012 ILSVRC \\  & Convolutional [130] Neural Networks & challenge. Similar to LeNet but deeper, bigger, and featured \\  & & convolutional networks. \\ \hline \multirow{2}{*}{ZF Net} & Visualizing and Understanding & [PERSON] and [PERSON], ILSVRC, 2013. Expanding the size of the middle convolutional layers and making the stride and filter size on the first layer smaller. \\ \hline \multirow{2}{*}{GoogLeNet} & Going Deeper with Convolutions [132] & [PERSON] et al., from Google, ILSVRC 2014. Going Deeper with \\  & & Convolutions \\ \hline \multirow{2}{*}{VGGNet} & Very Deep Convolutional Networks for & [PERSON] and [PERSON] in ILSVRC 2014. The \\  & Large-Scale Image Recognition [133] & depth of the network is a critical component for good performance. \\ \hline \multirow{2}{*}{ResNet} & Residual Network [134] & [PERSON] et al., in ILSVRC 2015. It features special skip connections and a heavy use of batch normalization. \\ \hline \multirow{2}{*}{Highway nets} & Highway Networks [135] & The architecture is characterized by the use of gating units that \\  & & learn to regulate the flow of information through a network. \\ \hline \multirow{2}{*}{DenseNet} & Densely Connected Convolutional & The dense convolutional network (DenseNet) connects each layer to every other layer in a feed-forward fashion. \\  & Networks [136] & \\ \hline \multirow{2}{*}{SENets} & Squeeze-and-Excitation Networks [137] & Model interdependencies between channels of the relationship of \\  & & features used in a traditional CNN \\ \hline \multirow{2}{*}{NASNet} & Neural Architecture Search Network [138] & Authors propose to search an architectural building block on a \\  & [138] & small dataset and then transfer the block to a larger dataset. \\ \hline \multirow{2}{*}{YOLO} & You Only Look Once [139] & A unified model for object detection \\ \hline \multirow{2}{*}{GANs} & Generative Adversarial Networks [120] & Framework for estimating generative models via adversarial nets \\ \hline \multirow{4}{*}{Siamese nets} & \multirow{4}{*}{Siamese Networks [140]} & A Siamese neural network is a class of neural network architectures that contain two or more identical subnetworks. The word identical \\  & & here means that they have the same configuration with the same \\  & & parameters and weights. \\ \hline \multirow{2}{*}{U-Net} & U-Net: Convolutional Networks for & The architecture consists of a contracting path to capture context \\  & Biomedical Image Segmentation [141] & and a symmetric expanding path that enables precise localization. \\ \hline \multirow{2}{*}{V-net} & V-Net: Fully Convolutional Neural & \multirow{2}{*}{Architecture for 3D image segmentation based on a volumetric, fully convolutional neural network} \\  & Networks for Volumetric Medical Image & \\  & Segmentation [142] & \\ \hline \multicolumn{4}{c}{**Libraries Used to Build DL Models**} \\ \hline \multirow{2}{*}{GIMIAS} & \multirow{2}{*}{[[http://www.gimias.org/](http://www.gimias.org/)]([http://www.gimias.org/](http://www.gimias.org/)) (accessed on 10 December 2020)} & A workflow-oriented environment for solving advanced, \\  & & biomedical image computing and individualized \\  & & simulation problems \\ \hline \multirow{2}{*}{SPM} & \multirow{2}{*}{[[https://www.fil.ion.ucl.ac.uk/spm/](https://www.fil.ion.ucl.ac.uk/spm/)]([https://www.fil.ion.ucl.ac.uk/spm/](https://www.fil.ion.ucl.ac.uk/spm/))} & The analysis of brain imaging data sequences using statistical \\  & & parametric mapping as an assessment of spatially extended \\  & (accessed on 10 December 2020) & statistical processes used to test hypotheses about functional \\  & & imaging data \\ \hline \multirow{2}{*}{FSL} & \multirow{2}{*}{[[https://fsl.fmrib.ox.ac.uk](https://fsl.fmrib.ox.ac.uk)]([https://fsl.fmrib.ox.ac.uk](https://fsl.fmrib.ox.ac.uk)) (accessed on 10 December 2020)} & A collection of analysis tools for functional magnetic resonance \\  & & images (fMRI), MRI, and diffusion tensor imaging (DTI) brain \\  & & imaging data \\ \hline \multirow{2}{*}{PyBrain} & \multirow{2}{*}{[[http://pybrain.org/](http://pybrain.org/)]([http://pybrain.org/](http://pybrain.org/)) (accessed on 10 December 2020)} & Reinforcement learning, artificial intelligence, and neural \\  & & network library \\ \hline \hline \end{tabular}
\end{table}