| CPC G16H 10/60 (2018.01) [G06T 7/00 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] | 12 Claims |
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1. A computer implemented method for generating a medical output image associated with a new event, the method comprising:
storing a plurality of past events, each of the plurality of past events including a plurality of past input attributes and a quantifiable outcome; and
training a neural network model (NNM) to generate a trained model, wherein the training of the NNM includes:
performing pre-processing on the plurality of past input attributes for each of the plurality of past events to generate a plurality of past input data sets;
dividing the plurality of past events into a first set of training data and a second set of validation data;
iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data; and
validating the NNM based upon the second set of validation data, receiving a plurality of input attributes of the new event, the input attributes associated with a plurality of segmented images;
performing pre-processing on the plurality of input attributes to generate an input data set; and
generating the medical output image from the trained model based upon the input data set, the medical output image being a reconstruction generated from the plurality of segmented images.
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