公开(公告)号：US11688517B2

公开(公告)日：2023-06-27

申请号：US17085005

申请日：2020-10-30

Applicant: Guerbet

Inventor： Thomas Binder ,  Giovanni John Jacques Palma

IPC: G16H50/20 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60 ,  G06F16/55 ,  A61B6/03 ,  A61B6/00 ,  G06N20/00 ,  G06F18/21 ,  G06F18/2431

CPC classification number: G16H50/20 ,  A61B6/032 ,  A61B6/50 ,  A61B6/5211 ,  G06F16/55 ,  G06F18/217 ,  G06F18/2431 ,  G06N20/00 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

Abstract: A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

公开(公告)号：US11688065B2

公开(公告)日：2023-06-27

申请号：US17740909

申请日：2022-05-10

Applicant: Guerbet

Inventor： Giovanni John Jacques Palma ,  Pedro Luis Esquinas Fernandez ,  Paul Dufort ,  Thomas Binder ,  Arkadiusz Sitek ,  Dana Levanony ,  Yi-Qing Wang ,  Omid Bonakdar Sakhi

IPC: G06T7/00 ,  G06T7/11

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30056 ,  G06T2207/30096

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.