公开(公告)号：US12039007B2

公开(公告)日：2024-07-16

申请号：US17067179

申请日：2020-10-09

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Andre De Almeida Maximo ,  Radhika Madhavan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06F18/214 ,  G06F18/211 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40

CPC classification number: G06F18/2148 ,  G06F18/211 ,  G06F18/2155 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40 ,  G06V2201/03

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.

公开(公告)号：US20230094940A1

公开(公告)日：2023-03-30

申请号：US17486796

申请日：2021-09-27

Applicant: GE Precision Healthcare LLC

Inventor： Radhika Madhavan ,  Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Andre De Almeida Maximo ,  Chitresh Bhushan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06N3/08 ,  G06K9/62

Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

公开(公告)号：US20220114389A1

公开(公告)日：2022-04-14

申请号：US17067179

申请日：2020-10-09

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Andre De Almeida Maximo ,  Radhika Madhavan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06K9/62 ,  G06N3/08 ,  G16H30/40

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.