Man pages for ANTsX/ANTsRNet
Neural Networks for Medical Image Processing
| allenEx5BrainExtraction | Perform brain extraction of Allen's E13.5 and E15.5 mouse... |
| allenHistologyBrainMask | Determine brain foreground of Allen's mouse embroyonic data. |
| AnchorBoxLayer2D | Anchor box layer for SSD architecture (2-D). |
| AnchorBoxLayer3D | Anchor box layer for SSD architecture (3-D). |
| applyDeepBackProjectionModel | Apply a pretrained deep back projection model for super... |
| applySuperResolutionModel | applySuperResolutionModel |
| applySuperResolutionModelPatch | applySuperResolutionModelPatch |
| applySuperResolutionModelToImage | Apply a pretrained model for super resolution. |
| arterialLesionSegmentation | Arterial lesion segmentation |
| AttentionAugmentationLayer2D | Attention augmentation layer (2-D) |
| AttentionLayer2D | Attention layer (2-D) |
| AttentionLayer3D | Attention layer (3-D) |
| basisWarp | Generate a deformable map from basis |
| binary_dice_coefficient | Dice function for binary segmentation problems |
| binary_surface_loss | Function for surface loss |
| brainAge | BrainAGE |
| brainExtraction | Brain extraction |
| brainTumorSegmentation | Brain tumor probabilistic segmentation |
| categorical_focal_gain | Function for categorical focal gain |
| categorical_focal_loss | Function for categorical focal loss |
| cerebellumMorphology | Cerebellum tissue segmentation, Schmahmann parcellation, and... |
| checkXrayLungOrientation | Check x-ray lung orientation. |
| chexnet | CheXNet: Radiologist-Level Pneumonia Detection on Chest... |
| claustrumSegmentation | Claustrum segmentation |
| ClusteringLayer | Clustering layer for Deep Embedded Clustering |
| ContextualAttentionLayer2D | Contextual attention layer (2-D) |
| ContextualAttentionLayer3D | Contextual attention layer (3-D) |
| convertCoordinates | Convert coordinates to/from min/max representation from/to... |
| convOutputLength | Compute output length in single dimension for convolution... |
| corticalThickness | Cortical thickness using deep learning |
| createAlexNetModel2D | 2-D implementation of the AlexNet deep learning architecture. |
| createAlexNetModel3D | 3-D implementation of the AlexNet deep learning architecture. |
| createAutoencoderModel | Function for creating a symmetric autoencoder model. |
| createConvolutionalAutoencoderModel2D | Function for creating a 2-D symmetric convolutional... |
| createConvolutionalAutoencoderModel3D | Function for creating a 3-D symmetric convolutional... |
| createDeepBackProjectionNetworkModel2D | 2-D implementation of the deep back-projection network. |
| createDeepBackProjectionNetworkModel3D | 3-D implementation of the deep back-projection network. |
| createDeepDenoiseSuperResolutionModel2D | 2-D implementation of the deep denoise image super resolution... |
| createDeepDenoiseSuperResolutionModel3D | 3-D implementation of the deep denoise image super resolution... |
| createDenoisingAutoEncoderSuperResolutionModel2D | 2-D implementation of the denoising autoencoder image super... |
| createDenoisingAutoEncoderSuperResolutionModel3D | 3-D implementation of the denoising autoencoder image super... |
| createDenseModel | Simple multilayer dense network. |
| createDenseNetModel2D | 2-D implementation of the DenseNet deep learning... |
| createDenseNetModel3D | 3-D implementation of the DenseNet deep learning... |
| createDenseUnetModel2D | 2-D implementation of the dense U-net deep learning... |
| createDenseUnetModel3D | 3-D implementation of the dense U-net deep learning... |
| createEnhancedDeepSuperResolutionModel2D | 2-D implementation of the EDSR super resolution architecture. |
| createExpandedSuperResolutionModel2D | 2-D implementation of the expanded image super resolution... |
| createExpandedSuperResolutionModel3D | 3-D implementation of the expanded image super resolution... |
| createFullyConvolutionalVggModel2D | 2-D implementation of the VGG deep learning architecture... |
| createFullyConvolutionalVggModel3D | 3-D implementation of the VGG deep learning architecture... |
| createGoogLeNetModel2D | 2-D implementation of the GoogLeNet deep learning... |
| createHippMapp3rUnetModel3D | Implementation of the "HippMapp3r" U-net architecture |
| createHyperMapp3rUnetModel3D | Implementation of the "HyperMapp3r" U-net architecture |
| createHypothalamusUnetModel3D | 3-D u-net architecture for hypothalamus segmentation |
| createImageSuperResolutionModel2D | 2-D implementation of the image super resolution deep... |
| createImageSuperResolutionModel3D | 3-D implementation of the image super resolution deep... |
| createNoBrainerUnetModel3D | Implementation of the "NoBrainer" U-net architecture |
| createPartialConvolutionUnetModel2D | 2-D implementation of the U-net architecture for inpainting... |
| createPartialConvolutionUnetModel3D | 3-D implementation of the U-net architecture for inpainting... |
| createResNetModel2D | 2-D implementation of the ResNet deep learning architecture. |
| createResNetModel3D | 3-D implementation of the ResNet deep learning architecture. |
| createResNetSuperResolutionModel2D | 2-D implementation of the ResNet image super resolution... |
| createResNetSuperResolutionModel3D | 3-D implementation of the ResNet image super resolution... |
| createResNetWithSpatialTransformerNetworkModel2D | 2-D implementation of the ResNet deep learning architecture... |
| createResNetWithSpatialTransformerNetworkModel3D | 3-D implementation of the ResNet deep learning architecture... |
| createResUnetModel2D | 2-D implementation of the Resnet + U-net deep learning... |
| createResUnetModel3D | 3-D implementation of the Resnet + U-net deep learning... |
| createRmnetGenerator | Implementation of the "RMNet" generator architecture for... |
| createSimpleClassificationWithSpatialTransformerNetworkModel2D | 2-D implementation of the spatial transformer network. |
| createSimpleClassificationWithSpatialTransformerNetworkModel3D | 3-D implementation of the spatial transformer network. |
| createSimpleFullyConvolutionalNeuralNetworkModel3D | Implementation of the "SCFN" architecture for Brain/Gender... |
| createSsd7Model2D | 2-D implementation of the SSD 7 deep learning architecture. |
| createSsd7Model3D | 3-D implementation of the SSD 7 deep learning architecture. |
| createSsdModel2D | 2-D implementation of the SSD deep learning architecture. |
| createSsdModel3D | 3-D implementation of the SSD deep learning architecture. |
| createSysuMediaUnetModel2D | Implementation of the sysu_media U-net architecture |
| createSysuMediaUnetModel3D | 3-D variant of the sysu_media U-net architecture |
| createUnetModel2D | 2-D implementation of the U-net deep learning architecture. |
| createUnetModel3D | 3-D image segmentation implementation of the U-net deep... |
| createVggModel2D | 2-D implementation of the VGG deep learning architecture. |
| createVggModel3D | 3-D implementation of the VGG deep learning architecture. |
| createWideResNetModel2D | 2-D implementation of the Wide ResNet deep learning... |
| createWideResNetModel3D | 3-D implementation of the Wide ResNet deep learning... |
| cropImageCenter | Crop the center of an image. |
| CycleGanModel | Cycle GAN model |
| dataAugmentation | Randomly transform image data. |
| decodeSsd2D | Decoding function for 2-D Y_train |
| decodeSsd3D | Decoding function for 3-D Y_train |
| decodeUnet | Decoding function for the u-net prediction outcome |
| deepAtropos | Six tissue segmentation |
| DeepConvolutionalGanModel | Deep convolutional GAN (DCGAN) model |
| DeepEmbeddedClusteringModel | Deep embedded clustering (DEC) model class |
| deepFlash | Hippocampal/Enthorhinal segmentation using "Deep Flash" |
| deepFlashDeprecated | Hippocampal/Enthorhinal segmentation using "Deep Flash" |
| desikanKillianyTourvilleLabeling | Cortical and deep gray matter labeling using... |
| drawRectangles | Plotting function for 2-D object detection visualization. |
| EfficientAttentionLayer2D | Efficient attention layer (2-D) |
| EfficientAttentionLayer3D | Efficient attention layer (3-D) |
| elBicho | Functional lung segmentation. |
| encodeSsd2D | Encoding function for 2-D Y_train |
| encodeSsd3D | Encoding function for 3-D Y_train |
| encodeUnet | One-hot encoding function |
| extractImagePatchCoordinates | Extract 2-D or 3-D image patch coordinates. |
| extractImagePatches | Extract 2-D or 3-D image patches. |
| GaussianDiffusion | Custom Gaussian Diffusion |
| getANTsXNetData | getANTsXNetData |
| getMixtureDensityLossFunction | Returns a loss function for the mixture density. |
| getMixtureDensityMseAccuracyFunction | Returns a MSE accuracy function for the mixture density. |
| getMixtureDensitySamplingFunction | Returns a sampling function for the mixture density. |
| getPretrainedNetwork | getPretrainedNetwork |
| GMSD | Gradient Magnitude Similarity Deviation |
| hippMapp3rSegmentation | hippMapp3rSegmentation |
| histogramWarpImageIntensities | Transform image intensities based on histogram mapping. |
| hyperMapp3rSegmentation | hyperMapp3rSegmentation |
| hypothalamusSegmentation | Hypothalamus segmentation |
| ImprovedWassersteinGanModel | Improved Wasserstein GAN model |
| InpaintingDeepFillModel | In-painting with contextual attention |
| InstanceNormalizationLayer | Creates an instance normalization layer |
| jaccardSimilarity | Jaccard similarity between two sets of boxes. |
| L2NormalizationLayer2D | L2 2-D normalization layer for SSD300/512 architecture. |
| L2NormalizationLayer3D | L2 3-D normalization layer for SSD300/512 architecture. |
| layer_activation_log_softmax | Log softmax layer |
| layer_anchor_box_2d | Anchor box layer (2-D and 3-D) |
| layer_attention_2d | Attention layer (2-D) |
| layer_attention_3d | Attention layer (3-D) |
| layer_attention_augmentation_2d | Attention augmentation layer (2-D) |
| layer_attention_augmented_convolution_block_2d | Creates a 2-D attention augmented convolutional block |
| layer_contextual_attention_2d | Contextual attention layer (2-D and 3-D) |
| layer_efficient_attention_2d | Efficient attention layer (2-D) |
| layer_efficient_attention_3d | Efficient attention layer (3-D) |
| layer_instance_normalization | Instance normalization layer |
| layer_l2_normalization_2d | Normalization layer (2-D and 3-D) |
| layer_mixture_density | Mixture density layer |
| layer_partial_conv_2d | Partial convolution layer 2D |
| layer_partial_conv_3d | Partial convolution layer 3D |
| layer_resample_tensor_2d | Creates a resampled tensor (to fixed size) layer (2-D) |
| layer_resample_tensor_3d | Resampling a spatial tensor (3-D). |
| layer_resample_tensor_to_target_tensor_2d | Resampling a spatial tensor to a target tensor (2-D). |
| layer_resample_tensor_to_target_tensor_3d | Resampling a spatial tensor (3-D). |
| layer_spatial_transformer_2d | spatial transformer layer (2-D and 3-D) |
| lesionSegmentation | wholeHeadInpainting |
| linMatchIntensity | linMatchIntensity |
| LogSoftmaxLayer | Creates a log softmax layer |
| longitudinalCorticalThickness | Longitudinal cortical thickness using deep learning |
| LossSSD | Loss function for the SSD deep learning architecture. |
| lungAirwaySegmentation | Lung airway segmentation. |
| lungExtraction | Lung extraction |
| lungPulmonaryArterySegmentation | Pulmonary artery segmentation. |
| MAE | Mean absolute error of a single image or between two images. |
| masked_mse_error | Function for masked mean-squared error |
| maximum_mean_discrepancy | Function for maximum-mean discrepancy |
| MixtureDensityNetworkLayer | Mixture density network layer |
| mixture_density_network_softmax | Softmax function for mixture density with temperature... |
| mouseBrainExtraction | Mouse brain extraction |
| mouseBrainParcellation | Mouse brain parcellation |
| mouseCorticalThickness | Mouse brain cortical thickness using deep learning |
| mriModalityClassification | MRI modality classification |
| mriSuperResolution | Super-resolution for MRI |
| MSE | Mean square error of a single image or between two images. |
| multilabel_dice_coefficient | Dice function for multilabel segmentation problems |
| neuralStyleTransfer | Neural transfer style |
| padImageByFactor | Pad an image based on a factor. |
| padOrCropImageToSize | Pad or crop image to a specified size |
| PartialConv2DLayer | Creates 2D partial convolution layer |
| PartialConv3DLayer | Creates 3D partial convolution layer |
| peak_signal_to_noise_ratio | Function to calculate peak-signal-to-noise ratio. |
| pearson_correlation_coefficient | Function for Pearson correlation coefficient. |
| pipe | Pipe operator |
| preprocessBrainImage | Basic preprocessing pipeline for T1-weighted brain MRI |
| PSNR | Peak signal-to-noise ratio between two images. |
| randomImageTransformAugmentation | Apply random transforms to a predictor / outcome training... |
| randomImageTransformBatchGenerator | Random image transformation batch generator |
| randomImageTransformParametersAugmentation | Generate transform parameters and transformed images |
| randomImageTransformParametersBatchGenerator | Random image transform parameters batch generator |
| randomlyTransformImageData | Randomly transform image data (optional: with corresponding... |
| reconstructImageFromPatches | Reconstruct image from a list of patches. |
| regressionMatchImage | Image intensity normalization using linear regression. |
| ResampleTensorLayer2D | Creates a resample tensor layer (2-D) |
| ResampleTensorLayer3D | Creates a resampled tensor (to fixed size) layer (3-D) |
| ResampleTensorToTargetTensorLayer2D | Creates a resampled tensor (to target tensor) layer (2-D) |
| ResampleTensorToTargetTensorLayer3D | Creates a resampled tensor (to target tensor) layer (3-D) |
| sampleFromCategoricalDistribution | Sample from a categorical distribution |
| sampleFromOutput | Sample from a distribution |
| ScaleLayer | Custom scale layer |
| simulateBiasField | Simulate random bias field |
| SpatialTransformerLayer2D | Spatial transformer layer (2-D) |
| SpatialTransformerLayer3D | Spatial transfomer layer (3-D) |
| splitMixtureParameters | Splits the mixture parameters. |
| SSIM | Structural similarity index (SSI) between two images. |
| SuperResolutionGanModel | Super resolution GAN model |
| sysuMediaWmhSegmentation | White matter hyperintensity segmentation |
| tidNeuralImageAssessment | Perform MOS-based assessment of an image. |
| uvaSeg | Unsupervised variational autoencoder segmentation |
| uvaSegTrain | Unsupervised variational autoencoder training |
| VanillaGanModel | Vanilla GAN model |
| WassersteinGanModel | Wasserstein GAN model |
| weighted_categorical_crossentropy | Function for weighted categorical cross entropy |
| wholeHeadInpainting | wholeHeadInpainting |
| wmhSegmentation | White matter hyperintensity probabilistic segmentation |
