公开(公告)号：US20230306601A1

公开(公告)日：2023-09-28

申请号：US17656171

申请日：2022-03-23

Applicant: GE Precision Healthcare LLC

Inventor： László Ruskó ,  Vanda Czipczer ,  Bernadett Kolozsvári ,  Richárd Zsámboki ,  Tao Tan ,  Balázs Péter Cziria ,  Attila Márk Rádics ,  Lehel Ferenczi ,  Fei Mian ,  Hongxiang YI ,  Florian Wiesinger

IPC: G06T7/11 ,  G06T7/00 ,  G06T7/12 ,  G06T11/20 ,  G06T11/00 ,  G16H30/40 ,  G16H30/20

CPC classification number: G06T7/11 ,  G06T7/0012 ,  G06T7/12 ,  G06T11/203 ,  G06T11/001 ,  G16H30/40 ,  G16H30/20 ,  G06T2207/30168

Abstract: Methods and systems are provided for segmenting structures in medical images. In one embodiment, a method includes receiving an input dataset including a set of medical images, a structure list specifying a set of structures to be segmented, and a segmentation protocol, performing an input check on the input dataset, determining whether each medical image of the set of medical images has passed the input check and removing any medical images from the set of medical images that do not pass the input check to form a final set of medical images, segmenting each structure from the structure list using one or more segmentation models and the final set of medical images, receiving a set of segmentations output from the one or more segmentation models, processing the set of segmentations to generate a final set of segmentations, and displaying and/or saving in memory the final set of segmentations.

公开(公告)号：US20220253708A1

公开(公告)日：2022-08-11

申请号：US17174049

申请日：2021-02-11

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Kumar Tamada ,  Junpyo Hong ,  Attila Márk Rádics ,  Hans Krupakar ,  Venkata Ratnam Saripalli ,  Dibyajyoti Pati ,  Guarav Kumar

IPC: G06N3/08 ,  G06N3/063 ,  G06N5/04 ,  G06K9/62

Abstract: Techniques are provided for compressing deep neural networks using a structured filter pruning method that is extensible and effective. According to an embodiment, a computer-implemented method comprises determining, by a system operatively coupled to a processor, importance scores for filters of layers of a neural network model previously trained until convergence for an inferencing task on a training dataset. The method further comprises removing, by the system, a subset of the filters from one or more layers of the layers based on the importance scores associated with the subset failing to satisfy a threshold importance score value. The method further comprises converting, by the system, the neural network model into a compressed neural network model with the subset of the filters removed.