# Adversarial Inpainting of Medical Image Modalities

arXiv: https://arxiv.org/abs/1810.06621 · HTML: https://mlsanity.com/paper/1810.06621/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 16 of 16)

- [Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report](https://mlsanity.com/paper/2210.12113/) (2022)
- [Sketch-guided Image Inpainting with Partial Discrete Diffusion Process](https://mlsanity.com/paper/2404.11949/) (2024)
- [Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning](https://mlsanity.com/paper/2103.07279/) (2021)
- [An Ensemble Approach for Brain Tumor Segmentation and Synthesis](https://mlsanity.com/paper/2411.17617/) (2024)
- [Good Things Come in Pairs: Paired Autoencoders for Inverse Problems](https://mlsanity.com/paper/2505.06549/) (2025)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images](https://mlsanity.com/paper/2204.02591/) (2022)
- [Shape-Aware Masking for Inpainting in Medical Imaging](https://mlsanity.com/paper/2207.05787/) (2022)
- [Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting](https://mlsanity.com/paper/2411.03098/) (2024)
- [DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum](https://mlsanity.com/paper/2307.00324/) (2023)
- [An inpainting approach to manipulate asymmetry in pre-operative breast images](https://mlsanity.com/paper/2502.05652/) (2025)
- [Using Synthetic Images to Augment Small Medical Image Datasets](https://mlsanity.com/paper/2503.00962/) (2025)
- [Investigating the Role of Bilateral Symmetry for Inpainting Brain MRI](https://mlsanity.com/paper/2504.10039/) (2025)
- [Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods](https://mlsanity.com/paper/2505.06811/) (2025)
- [Improving Diagnostic Accuracy for Oral Cancer with inpainting Synthesis Lesions Generated Using Diffusion Models](https://mlsanity.com/paper/2508.06151/) (2025)
- [Generative deep learning for foundational video translation in ultrasound](https://mlsanity.com/paper/2511.03255/) (2025)

## Abstract (arXiv)

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities.