Abstract: This disclosure is directed to methods and systems for protecting a deep learning model from piracy and unauthorized uses. The protection may be implemented by embedding an ownership detection mechanism such that unauthorized use of the model may be detected using a detection input data and corresponding model signature. In addition, the deep learning model may be used in conjunction with a secret or license protected data encoder such that the deep learning model may generate meaningful output only when processing encoded input data. An unauthorized user who does not have access to the secret data encoder may not be able to use a pirated copy of the deep learning model to generate meaningful output. Under such a scheme, a deep learning model itself may be widely distributed without restriction and without license-protection.

Abstract: This disclosure relates to digital image segmentation, region of interest identification, and object recognition. This disclosure describes a method, a system, for image segmentation based on fully convolutional neural network including an expansion neural network and contraction neural network. The various convolutional and deconvolution layers of the neural networks are architected to include a coarse-to-fine residual learning module and learning paths, as well as a dense convolution module to extract auto context features and to facilitate fast, efficient, and accurate training of the neural networks capable of producing prediction masks of regions of interest. While the disclosed method and system are applicable for general image segmentation and object detection/identification, they are particularly suitable for organ, tissue, and lesion segmentation and detection in medical images.

Abstract: This disclosure is directed to a system and a method for providing enhanced real-time or near-real-time response to request for detached data analytics services. In one implementation, a system is disclosed for predicting a data analytics service that may be requested by a user based on real-time user interactive operations, and for pre-loading/pre-configuring a pipeline of data analytics components for performing the predicted data analytics service before an actual request is made. Additionally, at least some intermediate data may be calculated by the pre-configured pipeline and may be pre-cached in memory. Upon actual user request for the data analytics service, only data analytics that require additional input data concurrently provided with the request would need to be performed. In such a manner, user-perceived delay in completing the detached data analytics service is reduced.

Abstract: This disclosure relates to digital image segmentation, region of interest identification, and object recognition. This disclosure describes a method, a system, for image segmentation based on fully convolutional neural network including an expansion neural network and contraction neural network. The various convolutional and deconvolution layers of the neural networks are architected to include a coarse-to-fine residual learning module and learning paths, as well as a dense convolution module to extract auto context features and to facilitate fast, efficient, and accurate training of the neural networks capable of producing prediction masks of regions of interest. While the disclosed method and system are applicable for general image segmentation and object detection/identification, they are particularly suitable for organ, tissue, and lesion segmentation and detection in medical images.

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.

Abstract: Described are systems, media, and methods for applying deep convolutional neural networks to medical images to generate a real-time or near real-time diagnosis.