Patent ID: 11922629
Assignee: NOVA SCOTIA HEALTH AUTHORITY
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC G

Claim 0:
1. A method of implementing a convolutional neural network for generating cancer prediction maps based on processing of multiparametric magnetic resonance images, the method comprising:
defining an anisotropic encoder-decoder convolutional neural network for processing multiparametric magnetic resonance images, wherein the anisotropic encoder-decoder convolutional neural network is configured such that an output thereof is a pixelated prediction image map, with each pixel of the pixelated prediction image map configured to provide a binary determination of a presence or absence of cancer;
performing supervised transfer learning to pretrain the anisotropic encoder-decoder convolutional neural network;
training the anisotropic encoder-decoder convolutional neural network with a plurality of multiparametric magnetic resonance training image sets and respective ground truth training image sets, each multiparametric magnetic resonance training image set corresponding to a given subject and comprising a plurality of multiparametric magnetic resonance image slices; and
employing test-time-augmentation to test the anisotropic encoder-decoder convolutional neural network with a plurality of multiparametric magnetic resonance test image sets and respective ground truth test image sets;
wherein test-time-augmentation is performed, when processing a given slice of a multiparametric magnetic resonance test image set, according to the steps of:
applying a plurality of transformations to the multiparametric magnetic resonance images corresponding to the given slice, thereby obtaining a plurality of transformed multiparametric magnetic resonance images;
employing the anisotropic encoder-decoder convolutional neural network to generate, for each transformed multiparametric magnetic resonance image, an intermediate cancer prediction map;
rectifying each intermediate cancer prediction map by applying a respective inverse transformation, thereby obtaining a plurality of rectified intermediate cancer prediction maps; and
combining the plurality of rectified intermediate cancer prediction maps to generate a composite cancer prediction map.