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US Patent 10140544 Enhanced convolutional neural network for image segmentation

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10140544
Patent Inventor Names
Jiao Wang57
Tianyi Zhao57
Yunqiang Chen57
Dashan Gao57
Date of Patent
November 27, 2018
Patent Application Number
15943392
Date Filed
April 2, 2018
Patent Citations Received
‌
US Patent 12136030 System and method for adapting a neural network model on a hardware platform
1
‌
US Patent 11476004 System capable of establishing model for cardiac ventricular hypertrophy screening
‌
US Patent 11487288 Data synthesis for autonomous control systems
4
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US Patent 12014553 Predicting three-dimensional features for autonomous driving
5
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US Patent 12020476 Data synthesis for autonomous control systems
6
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US Patent 12079723 Optimizing neural network structures for embedded systems
7
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US Patent 12086097 Vector computational unit
8
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US Patent 12106856 Image processing apparatus, image processing method, and program for segmentation correction of medical image
9
...
Patent Primary Examiner
‌
Siamak Harandi
Patent abstract

This disclosure relates to digital image segmentation and region of interest identification. A computer implemented image segmentation method and system are particularly disclosed, including a predictive model trained based on a deep fully convolutional neural network. The model is trained using a loss function in at least one intermediate layer in addition to a loss function at the final stage of the full convolutional neural network. The predictive segmentation model trained in such a manner requires less training parameters and facilitates quicker and more accurate identification of relevant local and global features in the input image. In one implementation, the fully convolutional neural network is further supplemented with a conditional adversarial neural networks iteratively trained with the fully convolutional neural network as a discriminator measuring the quality of the predictive model generated by the fully convolutional neural network.

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