인공지능 바이오전자공학 연구실

Convergence Research Consortium: University-Enterprise-Hospital Joint Study to Obtain Medical Clinical Experiments and Data for Machine Learning of Virtual Company Platforms, and Proceed with Commercialization Research. Development of medical diagnosis platform using artificial intelligence technology. Many types of liver diseases and conditions injure healthy liver cells, causing cell death and inflammation. Cirrhosis is a late-stage result of liver disease and its complications. Cirrhosis keeps the liver from working properly and late-stage cirrhosis is life-threatening. We propose a fully automated liver steatosis prediction model using deep learning neural networks. Our model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts. A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data. Generative adversarial networks consist of two neural networks, the generator and the discriminator, which compete against each other. Semantic image segmentation is the task of classifying each pixel in an image from a predefined set of classes. In the following example, different entities are classified. In order to perform semantic segmentation, a higher level understanding of the image is required. The algorithm should figure out the objects present and also the pixels which correspond to the object. Semantic segmentation is one of the essential tasks for complete scene understanding. Interpolation refers to the process of smoothly transitioning between two different data poin