The technology disclosed uses a 3D deep convolutional neural network architecture (DCNNA) equipped with so-called subnetwork modules which perform dimensionality reduction operations on 3D radiological volume before the 3D radiological volume is subjected to computationally expensive operations. Also, the subnetworks convolve 3D data at multiple scales by subjecting the 3D data to parallel processing by different 3D convolutional layer paths. Such multi-scale operations are computationally cheaper than the traditional CNNs that perform serial convolutions. In addition, performance of the subnetworks is further improved through 3D batch normalization (BN) that normalizes the 3D input fed to the subnetworks, which in turn increases learning rates of the 3D DCNNA. After several layers of 3D convolution and 3D sub-sampling with 3D across a series of subnetwork modules, a feature map with reduced vertical dimensionality is generated from the 3D radiological volume and fed into one or more fully connected layers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of convoluting three-dimensional (3D) data in a deep neural network, the method including: receiving three-dimensional (3D) data characterizing an input radiological volume; processing the 3D data characterizing the input radiological volume using a deep neural network to generate alternative feature volume representations of the input radiological volume, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input radiological volume using the deep neural network comprises processing the data through each of the subnetworks in the sequence; wherein three or more of the subnetworks are module subnetworks and each of the module subnetworks is configured to: receive a preceding output representation generated by a preceding subnetwork in the sequence; process the preceding output representation through at least three parallel 3D convolution layer paths of varying convolution volume; further process the preceding output representation through a parallel 3D max pooling path; and concatenate output of the 3D convolution layer paths and the 3D max pooling path to generate an output representation from each of the module subnetworks; and following processing the data through the three or more module subnetworks, processing the output of the highest of the module subnetworks through a vertical max-pooling layer to generate an output of reduced vertical dimensionality from the input radiological volume. 2. The method of claim 1, wherein a first 3D convolution layer path is a 3×3×3 convolution that extracts semi-global features from the input radiological volume. 3. The method of claim 1, wherein a second 3D convolution layer path is a 5×5×5 convolution that extracts global features from the input radiological volume. 4. The method of claim 1, wherein a third 3D convolution layer path is a 1×1×1 convolution that extracts local features from the input radiological volume. 5. The method of claim 1, wherein the input radiological volume represents 3D anatomical shape variations of a human organ structure generated from medical scans. 6. The method of claim 1, wherein the input radiological volume represents 3D model data. 7. The method of claim 1, wherein the input radiological volume represents CAD (computer-aided drafting) model data. 8. The method of claim 1, wherein the input radiological volume represents 3D point clouds. 9. The method of claim 1, further including: conditioning the output representation of the 3D convolution layer paths in the subnetworks by 3D batch normalization, wherein the 3D batch normalization applies independently to respective features in the alternative feature volume representations and is applied on a batch basis, wherein, for the alternative feature value representations in a batch, the 3D batch normalization scales and shifts image data values such that normalized alternative feature volume representations have a zero mean and a unit variance of the image data values. 10. The method of claim 9, further including processing the normalized alternative feature volume representations immediately through a parameterized non-linear activation with learnable scaling and shifting parameters by which training of the deep neural network can counteract the 3D batch normalization. 11. A method of convoluting three-dimensional (3D) data in a deep neural network, the method including: receiving three-dimensional (3D) data characterizing an input radiological volume; processing the 3D data characterizing the input radiological volume using a deep neural network to generate alternative feature volume representations of the input radiological volume, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input radiological volume using the deep neural network comprises processing the data through each of the subnetworks in the sequence; wherein three or more of the subnetworks are module subnetworks and each of the module subnetworks is configured to: receive a preceding output representation generated by a preceding subnetwork in the sequence; for at least three parallel 3D convolution layer paths of varying convolution volume, process the preceding output representation at multiple scales of convolution values over a dimensionality reduction layer; further process the preceding output representation through a parallel 3D pooling path; and concatenate output of the 3D convolution layer paths and the 3D pooling path to generate an output representation from each of the module subnetworks; and following processing the data through the three or more module subnetworks, processing the output of the highest of the module subnetworks through a vertical pooling layer to generate an output of reduced vertical dimensionality from the input radiological volume. 12. The method of claim 11, wherein a first 3D convolution layer path is a 3×3×3 convolution that extracts semi-global features from the input radiological volume. 13. The method of claim 11, wherein a second 3D convolution layer path is a 5×5×5 convolution that extracts global features from the input radiological volume. 14. The method of claim 11, wherein the dimensional reduction layer is a 1×1×1 convolution that extracts local features from the input radiological volume. 15. The method of claim 11, further including applying one or more preparatory 3D and/or 2D convolutions on the input radiological volume prior to processing by the subnetworks. 16. The method of claim 11, further including applying one or more preparatory 3D and/or 2D pooling operations on the input radiological volume prior to processing by the subnetworks. 17. The method of claim 11, further including applying one or more post-processing 3D and/or 2D convolutions on the input radiological volume after processing by the subnetworks. 18. The method of claim 11, further including applying one or more post-processing 3D and/or 2D pooling operations on the input radiological volume after processing by the subnetworks. 19. The method of claim 11, further including: conditioning the output representation of the 3D convolution layer paths in the subnetworks by 3D batch normalization, wherein the 3D batch normalization applies independently to respective features in the alternative feature volume representations and is applied on a batch basis, wherein, for the alternative feature value representations in a batch, the 3D batch normalization scales and shifts image data values such that normalized alternative feature volume representations have a zero mean and a unit variance of the image data values. 20. The method of claim 19, further including processing the normalized alternative feature volume representations immediately through a parameterized non-linear activation with learnable scaling and shifting parameters by which training of the deep neural network can counteract the 3D batch normalization. 21. A non-transitory computer readable storage medium impressed with computer program instructions, the instructions, when executed on a processor, implement the method of claim 1. 22. A non-transitory computer readable storage medium impressed with computer program instructions, the instructions, when executed on a processor, implement the method of claim 9. 23. A non-transitory computer readable storage medium impressed with computer program instructions, the instructions, when executed on a processor, implement the method of claim 10. 24. A non-transitory computer readable storage medium impressed with computer program instructions, the instructions, when executed on a processor, implement the method of claim 11. 25. A system including one or more processors coupled to memory, the memory loaded with computer instructions, the instructions, when executed on the processors, implement actions of claim 1. 26. A system including one or more processors coupled to memory, the memory loaded with computer instructions, the instructions, when executed on the processors, implement actions of claim 9. 27. A system including one or more processors coupled to memory, the memory loaded with computer instructions, the instructions, when executed on the processors, implement actions of claim 10. 28. A system including one or more processors coupled to memory, the memory loaded with computer instructions, the instructions, when executed on the processors, implement actions of claim 11.