Abstract: Disclosed herein are a neural network model deployment method and apparatus for providing a deep learning service. The neural network model deployment method may include providing a specification wizard to a user, searching for and training a neural network based on a user requirement specification that is input through the specification wizard, generating a neural network template code based on the user requirement specification and the trained neural network, converting the trained neural network into a deployment neural network that is usable in a target device based on the user requirement specification, and deploying the deployment neural network to the target device.

Abstract: A learning method for generating a multiphase collateral image comprises the steps of: receiving inputs of an MRI image of a head part and a multiphase collateral image generated on the basis of the MRI image; generating a brain mask by using the MRI image; generating an MRI image and a multiphase collateral image which are masked by the brain mask; and learning the masked multiphase collateral image for the masked MRI image by using a learning network.

Abstract: Methods and systems for diagnosing cancer in the prostate and other organs are disclosed. Exemplary methods comprises extracting texture information from MRI imaging data for a target organ, sometimes using two or more different imaging modalities. Texture features are determined that are indicative of cancer by identifying frequent texture patterns. A classification model is generated based on the determined texture features that are indicative of cancer, and diagnostic cancer prediction information for the target organ is then generated to help diagnose cancer in the organ.

Abstract: This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.

Abstract: This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.

Abstract: The present disclosure relates to methods of diagnosing prostate cancer using different imaging methods. For example, it is shown herein that combining a Fourier transform infrared (FT-IR) spectroscopic image with an optical image (such as a hematoxylin and eosin image) allows for automated detection of prostate cancer with high accuracy.

Abstract: This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.

Abstract: This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.