公开(公告)号：US11669945B2

公开(公告)日：2023-06-06

申请号：US16858862

申请日：2020-04-27

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06F18/21 ,  G06V10/772 ,  G06V10/774 ,  G06V10/762 ,  G06V10/74 ,  G06V10/776 ,  G06T7/00 ,  G06V10/82 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06F18/214

CPC classification number: G06F18/217 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/761 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

公开(公告)号：US12051178B2

公开(公告)日：2024-07-30

申请号：US18304947

申请日：2023-04-21

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06T5/50 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/74 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06T5/50 ,  G06F18/214 ,  G06F18/217 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/761 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

公开(公告)号：US20230252614A1

公开(公告)日：2023-08-10

申请号：US18304947

申请日：2023-04-21

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06T5/50 ,  G06V10/772 ,  G06V10/774 ,  G06V10/762 ,  G06V10/74 ,  G06V10/776 ,  G06T7/00 ,  G06V10/82 ,  G06F18/21 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06F18/214

CPC classification number: G06T5/50 ,  G06V10/772 ,  G06V10/774 ,  G06V10/762 ,  G06V10/761 ,  G06V10/776 ,  G06T7/00 ,  G06V10/82 ,  G06F18/217 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06F18/214

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

公开(公告)号：US20210334598A1

公开(公告)日：2021-10-28

申请号：US16858862

申请日：2020-04-27

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06K9/62

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.