Abstract: An electroluminescent display device includes: a substrate, a first electrode on the substrate, a connection pattern on the substrate, the connection pattern including a same material as the first electrode, a bank covering edges of the first electrode and the connection pattern, a light-emitting layer on the first electrode, a second electrode on the light-emitting layer, the bank, and the connection pattern, and an auxiliary pattern between the connection pattern and the second electrode, the auxiliary pattern including one or more of: a metal oxide, conductive nanoparticles, and a work function-modifying polymer.

Abstract: A display device includes pixels, and a power supply to supply a first initialization voltage to the pixels through a first initialization power line, and to supply a second initialization voltage to the pixels through a second initialization power line. At least one of the pixels includes a first transistor, a second transistor, a third transistor, and a light emitting element.

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: 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.