US 12,333,716 B2
Generating high quality training data collections for training artificial intelligence models
Mahendra Madhukar Patil, Bangalore (IN); Rakesh Mullick, Bangalore (IN); Sudhanya Chatterjee, Bengaluru (IN); Syed Asad Hashmi, Bangalore (IN); Dattesh Dayanand Shanbhag, Bangalore (IN); Deepa Anand, Bangalore (IN); and Suresh Emmanuel Devadoss Joel, Bangalore (IN)
Assigned to GE Precision Healthcare LLC, Waukesha, WI (US)
Filed by GE Precision Healthcare LLC, Wauwatosa, WI (US)
Filed on Apr. 26, 2022, as Appl. No. 17/660,717.
Prior Publication US 2023/0342913 A1, Oct. 26, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06V 10/25 (2022.01)
CPC G06T 7/0012 (2013.01) [G06N 20/00 (2019.01); G06V 10/25 (2022.01); G06V 2201/03 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a clinical criteria selection component that receives first input indicating a clinical context associated with usage of a medical image dataset comprising a plurality of medical images;
a scrutiny criteria selection component that selects one or more image quality metrics for filtering the medical image dataset based on the clinical context, the one or more images quality metrics respectively related to a measure of medical image quality, wherein the one or more image quality metrics are selected from the group consisting of: signal to noise ratio, peak signal to noise ratio, mean square error, structural similarity index, feature similarity index, variance inflation factor and Laplacian loss;
an image processing component that applies one or more image processing functions to the medical image dataset to generate metric values of the one or more image quality metrics for respective medical images included in the medical image dataset;
a filtering component that filters the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values, the one or more subsets respectively comprising a portion of the medical images;
a visualization component that generates one or more graphical visualizations representative of the metric values for the respective medical images; and
a rendering component that renders the one or more graphical visualizations via an interactive graphical user interface, wherein the one or more acceptability criteria comprises acceptable values for the one or more metric values and wherein the one or more graphical visualizations distinguish the one or more subsets associated with the acceptable values from outlier images of the medical image dataset associated with unacceptable values.