| Anchor box layer for SSD architecture (2-D). | AnchorBoxLayer2D |
| Anchor box layer for SSD architecture (3-D). | AnchorBoxLayer3D |
| Apply a pretrained deep back projection model for super resolution. | applyDeepBackProjectionModel |
| applySuperResolutionModel | applySuperResolutionModel |
| applySuperResolutionModelPatch | applySuperResolutionModelPatch |
| Apply a pretrained model for super resolution. | applySuperResolutionModelToImage |
| Arterial lesion segmentation | arterialLesionSegmentation |
| Attention augmentation layer (2-D) | AttentionAugmentationLayer2D |
| Attention layer (2-D) | AttentionLayer2D |
| Attention layer (3-D) | AttentionLayer3D |
| Generate a deformable map from basis | basisWarp |
| Dice function for binary segmentation problems | binary_dice_coefficient |
| Function for surface loss | binary_surface_loss |
| BrainAGE | brainAge |
| Brain extraction | brainExtraction |
| MRA-TOF vessel segmentation. | brainMraVesselSegmentation |
| Brain tumor probabilistic segmentation | brainTumorSegmentation |
| Function for categorical focal gain | categorical_focal_gain |
| Function for categorical focal loss | categorical_focal_loss |
| Cerebellum tissue segmentation, Schmahmann parcellation, and thickness. | cerebellumMorphology |
| Check x-ray lung orientation. | checkXrayLungOrientation |
| CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning | chexnet |
| CIT168 Labeling | cit168Labeling |
| Claustrum segmentation | claustrumSegmentation |
| Clustering layer for Deep Embedded Clustering | ClusteringLayer |
| Contextual attention layer (2-D) | ContextualAttentionLayer2D |
| Contextual attention layer (3-D) | ContextualAttentionLayer3D |
| Convert coordinates to/from min/max representation from/to centroids/width | convertCoordinates |
| Compute output length in single dimension for convolution filter | convOutputLength |
| Cortical thickness using deep learning | corticalThickness |
| 2-D implementation of the AlexNet deep learning architecture. | createAlexNetModel2D |
| 3-D implementation of the AlexNet deep learning architecture. | createAlexNetModel3D |
| Function for creating a symmetric autoencoder model. | createAutoencoderModel |
| Function for creating a 2-D symmetric convolutional autoencoder model. | createConvolutionalAutoencoderModel2D |
| Function for creating a 3-D symmetric convolutional autoencoder model. | createConvolutionalAutoencoderModel3D |
| 2-D implementation of the deep back-projection network. | createDeepBackProjectionNetworkModel2D |
| 3-D implementation of the deep back-projection network. | createDeepBackProjectionNetworkModel3D |
| 2-D implementation of the deep denoise image super resolution architecture. | createDeepDenoiseSuperResolutionModel2D |
| 3-D implementation of the deep denoise image super resolution architecture. | createDeepDenoiseSuperResolutionModel3D |
| 2-D implementation of the denoising autoencoder image super resolution architecture. | createDenoisingAutoEncoderSuperResolutionModel2D |
| 3-D implementation of the denoising autoencoder image super resolution architecture. | createDenoisingAutoEncoderSuperResolutionModel3D |
| Simple multilayer dense network. | createDenseModel |
| 2-D implementation of the DenseNet deep learning architecture. | createDenseNetModel2D |
| 3-D implementation of the DenseNet deep learning architecture. | createDenseNetModel3D |
| 2-D implementation of the dense U-net deep learning architecture. | createDenseUnetModel2D |
| 3-D implementation of the dense U-net deep learning architecture. | createDenseUnetModel3D |
| 2-D implementation of the EDSR super resolution architecture. | createEnhancedDeepSuperResolutionModel2D |
| 2-D implementation of the expanded image super resolution architecture. | createExpandedSuperResolutionModel2D |
| 3-D implementation of the expanded image super resolution architecture. | createExpandedSuperResolutionModel3D |
| 2-D implementation of the VGG deep learning architecture without classification layers. | createFullyConvolutionalVggModel2D |
| 3-D implementation of the VGG deep learning architecture without classification layers. | createFullyConvolutionalVggModel3D |
| 2-D implementation of the GoogLeNet deep learning architecture. | createGoogLeNetModel2D |
| Implementation of the "HippMapp3r" U-net architecture | createHippMapp3rUnetModel3D |
| Implementation of the "HyperMapp3r" U-net architecture | createHyperMapp3rUnetModel3D |
| 3-D u-net architecture for hypothalamus segmentation | createHypothalamusUnetModel3D |
| 2-D implementation of the image super resolution deep learning architecture. | createImageSuperResolutionModel2D |
| 3-D implementation of the image super resolution deep learning architecture. | createImageSuperResolutionModel3D |
| Implementation of the "NoBrainer" U-net architecture | createNoBrainerUnetModel3D |
| 2-D implementation of the U-net architecture for inpainting using partial convolution. | createPartialConvolutionUnetModel2D |
| 3-D implementation of the U-net architecture for inpainting using partial convolution. | createPartialConvolutionUnetModel3D |
| 2-D implementation of the ResNet deep learning architecture. | createResNetModel2D |
| 3-D implementation of the ResNet deep learning architecture. | createResNetModel3D |
| 2-D implementation of the ResNet image super resolution architecture. | createResNetSuperResolutionModel2D |
| 3-D implementation of the ResNet image super resolution architecture. | createResNetSuperResolutionModel3D |
| 2-D implementation of the ResNet deep learning architecture with a preceding spatial transformer network layer. | createResNetWithSpatialTransformerNetworkModel2D |
| 3-D implementation of the ResNet deep learning architecture with a preceding spatial transformer network layer. | createResNetWithSpatialTransformerNetworkModel3D |
| 2-D implementation of the Resnet + U-net deep learning architecture. | createResUnetModel2D |
| 3-D implementation of the Resnet + U-net deep learning architecture. | createResUnetModel3D |
| Implementation of the "RMNet" generator architecture for inpainting | createRmnetGenerator |
| Implementation of the "shiva" u-net architecture for PVS and WMH segmentation | createShivaUnetModel3D |
| 2-D implementation of the spatial transformer network. | createSimpleClassificationWithSpatialTransformerNetworkModel2D |
| 3-D implementation of the spatial transformer network. | createSimpleClassificationWithSpatialTransformerNetworkModel3D |
| Implementation of the "SCFN" architecture for Brain/Gender prediction | createSimpleFullyConvolutionalNeuralNetworkModel3D |
| 2-D implementation of the SSD 7 deep learning architecture. | createSsd7Model2D |
| 3-D implementation of the SSD 7 deep learning architecture. | createSsd7Model3D |
| 2-D implementation of the SSD deep learning architecture. | createSsdModel2D |
| 3-D implementation of the SSD deep learning architecture. | createSsdModel3D |
| Implementation of the sysu_media U-net architecture | createSysuMediaUnetModel2D |
| 3-D variant of the sysu_media U-net architecture | createSysuMediaUnetModel3D |
| 2-D implementation of the U-net deep learning architecture. | createUnetModel2D |
| 3-D image segmentation implementation of the U-net deep learning architecture. | createUnetModel3D |
| 2-D implementation of the VGG deep learning architecture. | createVggModel2D |
| 3-D implementation of the VGG deep learning architecture. | createVggModel3D |
| 2-D implementation of the Wide ResNet deep learning architecture. | createWideResNetModel2D |
| 3-D implementation of the Wide ResNet deep learning architecture. | createWideResNetModel3D |
| Cycle GAN model | CycleGanModel |
| Decoding function for 2-D Y_train | decodeSsd2D |
| Decoding function for 3-D Y_train | decodeSsd3D |
| Six tissue segmentation | deepAtropos |
| Deep convolutional GAN (DCGAN) model | DeepConvolutionalGanModel |
| Deep embedded clustering (DEC) model class | DeepEmbeddedClusteringModel |
| Hippocampal/Enthorhinal segmentation using "Deep Flash" | deepFlash |
| Cortical and deep gray matter labeling using Desikan-Killiany-Tourville | desikanKillianyTourvilleLabeling |
| Plotting function for 2-D object detection visualization. | drawRectangles |
| Efficient attention layer (2-D) | EfficientAttentionLayer2D |
| Efficient attention layer (3-D) | EfficientAttentionLayer3D |
| Functional lung segmentation. | elBicho |
| Encoding function for 2-D Y_train | encodeSsd2D |
| Encoding function for 3-D Y_train | encodeSsd3D |
| Custom Gaussian Diffusion | GaussianDiffusion |
| Get the ANTsXNet Cache Directory | getANTsXNetCacheDirectory |
| getANTsXNetData | getANTsXNetData |
| Returns a loss function for the mixture density. | getMixtureDensityLossFunction |
| Returns a MSE accuracy function for the mixture density. | getMixtureDensityMseAccuracyFunction |
| Returns a sampling function for the mixture density. | getMixtureDensitySamplingFunction |
| getPretrainedNetwork | getPretrainedNetwork |
| Gradient Magnitude Similarity Deviation | GMSD |
| Subcortical and cerebellar labeling from a T1 image. | harvardOxfordAtlasLabeling |
| hippMapp3rSegmentation | hippMapp3rSegmentation |
| hyperMapp3rSegmentation | hyperMapp3rSegmentation |
| Hypothalamus segmentation | hypothalamusSegmentation |
| Improved Wasserstein GAN model | ImprovedWassersteinGanModel |
| In-painting with contextual attention | InpaintingDeepFillModel |
| Creates an instance normalization layer | InstanceNormalizationLayer |
| Jaccard similarity between two sets of boxes. | jaccardSimilarity |
| L2 2-D normalization layer for SSD300/512 architecture. | L2NormalizationLayer2D |
| L2 3-D normalization layer for SSD300/512 architecture. | L2NormalizationLayer3D |
| Log softmax layer | layer_activation_log_softmax |
| Anchor box layer (2-D and 3-D) | layer_anchor_box_2d layer_anchor_box_3d |
| Attention layer (2-D) | layer_attention_2d |
| Attention layer (3-D) | layer_attention_3d |
| Attention augmentation layer (2-D) | layer_attention_augmentation_2d |
| Creates a 2-D attention augmented convolutional block | layer_attention_augmented_convolution_block_2d |
| Contextual attention layer (2-D and 3-D) | layer_contextual_attention_2d layer_contextual_attention_3d |
| Efficient attention layer (2-D) | layer_efficient_attention_2d |
| Efficient attention layer (3-D) | layer_efficient_attention_3d |
| Instance normalization layer | layer_instance_normalization |
| Normalization layer (2-D and 3-D) | layer_l2_normalization_2d layer_l2_normalization_3d |
| Mixture density layer | layer_mixture_density |
| Partial convolution layer 2D | layer_partial_conv_2d |
| Partial convolution layer 3D | layer_partial_conv_3d |
| Creates a resampled tensor (to fixed size) layer (2-D) | layer_resample_tensor_2d |
| Resampling a spatial tensor (3-D). | layer_resample_tensor_3d |
| Resampling a spatial tensor to a target tensor (2-D). | layer_resample_tensor_to_target_tensor_2d |
| Resampling a spatial tensor (3-D). | layer_resample_tensor_to_target_tensor_3d |
| spatial transformer layer (2-D and 3-D) | layer_spatial_transformer_2d layer_spatial_transformer_3d |
| wholeHeadInpainting | lesionSegmentation |
| linMatchIntensity | linMatchIntensity |
| Creates a log softmax layer | LogSoftmaxLayer |
| Longitudinal cortical thickness using deep learning | longitudinalCorticalThickness |
| Loss function for the SSD deep learning architecture. | LossSSD |
| Lung airway segmentation. | lungAirwaySegmentation |
| Lung extraction | lungExtraction |
| Pulmonary artery segmentation. | lungPulmonaryArterySegmentation |
| Mean absolute error of a single image or between two images. | MAE |
| Function for masked mean-squared error | masked_mse_error |
| Function for maximum-mean discrepancy | maximum_mean_discrepancy |
| Softmax function for mixture density with temperature adjustment | mixture_density_network_softmax |
| Mixture density network layer | MixtureDensityNetworkLayer |
| Mouse brain extraction | mouseBrainExtraction |
| Mouse brain parcellation | mouseBrainParcellation |
| Mouse brain cortical thickness using deep learning | mouseCorticalThickness |
| MRI modality classification | mriModalityClassification |
| Super-resolution for MRI | mriSuperResolution |
| Mean square error of a single image or between two images. | MSE |
| Dice function for multilabel segmentation problems | multilabel_dice_coefficient |
| Nucleus Basalis of Meynert (NBM) Labeling | nbmLabeling |
| Neural transfer style | neuralStyleTransfer |
| Creates 2D partial convolution layer | PartialConv2DLayer |
| Creates 3D partial convolution layer | PartialConv3DLayer |
| Function to calculate peak-signal-to-noise ratio. | peak_signal_to_noise_ratio |
| Function for Pearson correlation coefficient. | pearson_correlation_coefficient |
| Basic preprocessing pipeline for T1-weighted brain MRI | preprocessBrainImage |
| Peak signal-to-noise ratio between two images. | PSNR |
| Apply random transforms to a predictor / outcome training image set | randomImageTransformAugmentation |
| Random image transformation batch generator | randomImageTransformBatchGenerator |
| Generate transform parameters and transformed images | randomImageTransformParametersAugmentation |
| Random image transform parameters batch generator | randomImageTransformParametersBatchGenerator |
| Creates a resample tensor layer (2-D) | ResampleTensorLayer2D |
| Creates a resampled tensor (to fixed size) layer (3-D) | ResampleTensorLayer3D |
| Creates a resampled tensor (to target tensor) layer (2-D) | ResampleTensorToTargetTensorLayer2D |
| Creates a resampled tensor (to target tensor) layer (3-D) | ResampleTensorToTargetTensorLayer3D |
| Sample from a categorical distribution | sampleFromCategoricalDistribution |
| Sample from a distribution | sampleFromOutput |
| Custom scale layer | ScaleLayer |
| Set the ANTsXNet Cache Directory | setANTsXNetCacheDirectory |
| Set ANTXNet deterministic behavior | setANTsXNetDeterministic |
| SHIVA PVS/VRS segmentation. | shivaPvsSegmentation |
| SHIVA WMH segmentation. | shivaWmhSegmentation |
| Spatial transformer layer (2-D) | SpatialTransformerLayer2D |
| Spatial transfomer layer (3-D) | SpatialTransformerLayer3D |
| Splits the mixture parameters. | splitMixtureParameters |
| Structural similarity index (SSI) between two images. | SSIM |
| Super resolution GAN model | SuperResolutionGanModel |
| White matter hyperintensity segmentation | sysuMediaWmhSegmentation |
| Supervised grader / scoring of T1-weighted brain MRI. | t1Grader |
| Perform MOS-based assessment of an image. | tidNeuralImageAssessment |
| Unsupervised variational autoencoder segmentation | uvaSeg |
| Unsupervised variational autoencoder training | uvaSegTrain |
| Vanilla GAN model | VanillaGanModel |
| Wasserstein GAN model | WassersteinGanModel |
| Function for weighted categorical cross entropy | weighted_categorical_crossentropy |
| wholeHeadInpainting | wholeHeadInpainting |
| White matter hyperintensity probabilistic segmentation | wmhSegmentation |
