Generation of findings in radiology reports by machine learning based on impressions
AssigneeSiemens Healthineers AG
InventorsOladimeji Farri, Ramya Vunikili
Life of this patent
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Abstract
Machine training is used to learn to generate findings in radiology reports. Rather than merely learning to output findings from an input, the machine training uses loss based on impression derived from findings to machine train the model to generate the findings. Once trained, the machine-learned model generates findings but the findings are more accurate or complete due to having used impression loss in the training.
Claims18
A method for generating a finding in radiology reports by a machine-learned system, the method comprising:
- obtaining a medical image of a patient;
- generating a first finding by a machine-learned model in response to input of the medical image to the machine-learned model, the machine-learned model trained, at least in part, from training data impressions based on training data findings, wherein the machine-learned model comprises a machine-learned first neural network configured to detect regions of interest and a machine-learned hierarchal recurrent neural network or a machine-learned bidirectional encoder representations from transformers network configured to generate the first finding from a projection of the detected regions of interest output by the machine-learned first neural network;
- and displaying the first finding.
A method for machine training to generate findings, the method comprising:
- defining a first model to receive images and output findings, wherein the first model is defined as a first neural network configured to detect regions of interest and a hierarchal recurrent neural network or a bidirectional encoder representations from transformers network configured to generate the findings from a projection of the detected regions of interest output by the first neural network;
- defining a second model to receive findings and output impressions;
- machine training the first model, at least in part, based on losses from the output impressions compared to ground truth impressions;
- and storing the machine-trained first model.
A system for creating an anatomical observation, the system comprising:
- a medical records database having stored therein an image of a patient;
- a processor configured to input the image to a machine-trained model configured to create a first anatomical observation in response to input of the image, the machine-trained model having been trained with a loss based on diagnostic conclusion derived from second anatomical observations, wherein the machine-trained model comprises a machine-trained first neural network configured to detect regions of interest and a machine-trained hierarchal recurrent neural network or a machine-trained bidirectional encoder representations from transformers network configured to generate the first anatomical observation from a projection of the detected regions of interest output by the machine-learned first neural network;
- and a display configured to output the first anatomical observation for the patient.
Dependencies are inferred from the claim text.