Deep learning methods and corresponding applications in medical imaging

Deep learning is a subfield of artificial intelligence and machine learning, and it is becoming a popular topic in recent years. It is a powerful tool in solving complex tasks and achieve state-of-the-art results in many areas, including language processing, computer vision, and more. This paper briefly introduced two main deep learning models, Convolutional Neural Networks (CNNs) and Generative Adversary Networks (GANs) and their applications in medical imaging. CNNs are often used in image recognition tasks, like separating different organs in one medical image. While GANs are better doing medical image generation tasks, like creating an X-ray image of chest. This study introduced some deep learning methods for image segmentation, image classification and image generation. Not only are the basic CNNs and GANs architectures used, but also some improvements and modification involved. These methods greatly expand the existing medical image datasets. They also save lots of time for doctors and radiologists from labeling and recognizing those medical images. Deep learning methods are super strong in processing complex and numerous medical images. However, there are still some limitations caused by the lack of training datasets and learning models.