公开(公告)号：US20240108299A1

公开(公告)日：2024-04-04

申请号：US17937152

申请日：2022-09-30

Applicant: GE Precision Healthcare LLC

Inventor： Masaki Ikuta ,  Junpyo Hong ,  Rajesh Kumar Tamada ,  Ravi Soni

IPC: A61B6/00 ,  G06T7/00

CPC classification number: A61B6/5258 ,  A61B6/5235 ,  G06T7/0012 ,  G06T2207/10081

Abstract: Computer processing techniques are described for augmenting computed tomography (CT) images with synthetic artifacts for artificial intelligence (AI) applications. According to an example, a computer-implemented method can include generating, by a system comprising a processor, synthetic artifact data corresponding to one or more CT image artifacts, wherein the synthetic artifact data comprises anatomy agnostic synthetic representations of the one or more CT image artifacts. The method further includes generating, by the system, augmented CT images comprising the one or more CT image artifacts using the synthetic artifact data. In one or more examples, the method can further include training, by the system, a medical image inferencing model to perform an inferencing task using the augmented CT images as training images.

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.