发明授权
- 专利标题: Characterization of amount of training for an input to a machine-learned network
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申请号: US16012151申请日: 2018-06-19
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公开(公告)号: US11630995B2公开(公告)日: 2023-04-18
- 发明人: Philipp Hoelzer , Sasa Grbic , Daguang Xu
- 申请人: Siemens Healthcare GmbH
- 申请人地址: DE Erlangen
- 专利权人: Siemens Healthcare GmbH
- 当前专利权人: Siemens Healthcare GmbH
- 当前专利权人地址: DE Erlangen
- 主分类号: G06F18/2411
- IPC分类号: G06F18/2411 ; G06N3/08 ; G16H50/20 ; G16H30/40 ; G06N3/02 ; G16H50/00 ; G16H50/70 ; G06V10/70 ; G06F18/22 ; G06F18/2415 ; G06V10/772 ; G06V10/778 ; G06V10/94
摘要:
The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.