Deep learning(DL) in the medical imaging field suffers from lack of usable data compared to natural image because of the private and sensitive nature of medical data. Also it is a highly imbalanced data because for almost any disease, medical imaging has more patients not having it rather than having it. To address these problems, synthetic data generation is considered to be a promising solution. In this study, we present Lesion In-aNd-Out Painting (LINOP) to generate synthetic medical images for data augmentation. Generative model based on Mask Aware Transformer (MAT) architecture was used to synthesize lesions onto normal image (inpainting) and synthesis outside of lesion area (outpainting). We train and validate a lesion inpainting pipeline on mammography dataset and a lesion outpainting pipeline on chest X-ray dataset. For mammography, proposed augmentation showed up to 30.3\% improvements on mass localization in terms of mAP@50, and for CXR, up to 10.3\% improvements on disease classification in terms of AUROC.