  Pseudo-normality synthesis, which computationally generates a pseudo-normal
image from an abnormal one (e.g., with lesions), is critical in many
perspectives, from lesion detection, data augmentation to clinical surgery
suggestion. However, it is challenging to generate high-quality pseudo-normal
images in the absence of the lesion information. Thus, expensive lesion
segmentation data have been introduced to provide lesion information for the
generative models and improve the quality of the synthetic images. In this
paper, we aim to alleviate the need of a large amount of lesion segmentation
data when generating pseudo-normal images. We propose a Semi-supervised Medical
Image generative LEarning network (SMILE) which not only utilizes limited
medical images with segmentation masks, but also leverages massive medical
images without segmentation masks to generate realistic pseudo-normal images.
Extensive experiments show that our model outperforms the best state-of-the-art
model by up to 6% for data augmentation task and 3% in generating high-quality
images. Moreover, the proposed semi-supervised learning achieves comparable
medical image synthesis quality with supervised learning model, using only 50
of segmentation data.
