CP‐GAN: Meet the high requirements of diagnose report to medical image by content preservation

Abstract

Medical image generation from diagnostic report has important research significance for medical‐aided diagnosis. This research can improve the diagnosis speed and accuracy of doctors and effectively save the storage resources of hospitals. The research has made significant progress in the field of natural images, but rarely used in medical images. Medical images have higher requirements for image quality. In this paper, a method based on attention mechanism and content preservation loss to improve image quality is proposed. The model consists of three stages, and each stage generates feature map of different scale combined with attention features as the input of the next stage to optimise semantic consistency between image and text. The last stage will generate an image, whose size is 256 × 256. And the content preservation loss can optimise the similarity between the generated image and the real image by low‐level and high‐level features to meet the high requirements of medical image for texture details. The content preservation loss consists of MSE loss, VGG loss and TV loss. The experiments on two datasets prove that the method can achieve excellent results.

Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks

“…In recent years, the analysis method of artificial intelligence combined with big data has been greatly developed and has strong momentum in the field of medical images [12]. Up to now, image recognition systems based on deep learning have covered almost all clinical stages, such as lesion detection, pathology diagnosis, radiotherapy planning, and postoperative prediction, and have gradually become an important auxiliary technical means for doctors' diagnosis [13][14].…”