A computer-implemented method of classifying three-dimensional data characterizing an input radiological volume in a deep neural network, the method including: receiving three-dimensional data characterizing an input radiological volume representing 3 D anatomical shape variations of a human organ structure generated from medical scans; 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; 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; and classifying the received 3D data based upon the generated output of reduced vertical dimensionality; wherein classifying the received 3D data based upon the generated output of reduced vertical dimensionality comprises classifying the input 3D data as a class of the set of classes consisting of: including an indication of hemorrhage; including evidence of possible Alzheimer's; including indications of a stroke; including an indication of fluid in the sinuses; including an indication of extracranial blood; including an indication of a skull fracture; and normal. receiving three-dimensional data characterizing an input radiological volume representing 3 D anatomical shape variations of a human organ structure generated from medical scans; 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; 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; 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; and classifying the received 3D data based upon the generated output of reduced vertical dimensionality; wherein classifying the received 3D data based upon the generated output of reduced vertical dimensionality comprises classifying the input 3D data as a class of the set of classes consisting of: including an indication of hemorrhage; including evidence of possible Alzheimer's; including indications of a stroke; including an indication of fluid in the sinuses; including an indication of extracranial blood; including an indication of a skull fracture; and normal.