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