US 12,450,736 B2
Identifying abnormal images in set of medical images based on combined feature set with global features extracted using first CNN and local features extracted using second CNN analyzing high resolution image patches
Vidya Madapusi Srinivas Prasad, Bangalore (IN); Srinivasa Rao Kundeti, Movva (IN); Manikanda Krishnan V, Bangalore (IN); and Vijayananda Jagannatha, Bangalore (IN)
Assigned to KONINKLIJKE PHLIPS N.V., Eindhoven (NL)
Appl. No. 17/922,809
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed May 3, 2021, PCT No. PCT/EP2021/061525
§ 371(c)(1), (2) Date Nov. 2, 2022,
PCT Pub. No. WO2021/224162, PCT Pub. Date Nov. 11, 2021.
Claims priority of application No. 20173286 (EP), filed on May 6, 2020.
Prior Publication US 2023/0334656 A1, Oct. 19, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 3/4053 (2024.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06T 7/0012 (2013.01) [G06T 3/4053 (2013.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06V 2201/031 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method of identifying abnormal images in a set of medical images for optimal assessment of the medical images, the method comprising:
extracting a plurality of global features from each medical image of the set of medical images based on pretrained weights associated with each of the plurality of global features, wherein the global features are assigned the pretrained weights using a first Convolutional Neural Network (CNN);
extracting, using a second Convolutional Neural Network (CNN) different from the first CNN, a plurality of local features from each medical image by analysing a predefined number of image patches corresponding to each medical image, wherein the predefined number of image patches are generated by obtaining a higher resolution image corresponding to each medical image and splitting each higher resolution image into the predefined number of image patches, wherein the respective image patches are analyzed to extract local features from the respective image patches, and wherein the extracted local features for the respective image patches are combined to obtain the plurality of local features, wherein a resolution of the medical image is less than a resolution of the higher resolution image;
determining an abnormality score for each medical image based on weights associated with a combined feature set obtained by concatenating the plurality of global features and the plurality of local features, wherein the abnormality score is determined using a pretrained feature classifier; and
identifying the medical image as an abnormal image when the abnormality score of the medical image is higher than a predefined first threshold score.