# Multi-Domain Image Completion for Random Missing Input Data

arXiv: https://arxiv.org/abs/2007.05534 · HTML: https://mlsanity.com/paper/2007.05534/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation](https://mlsanity.com/paper/2204.13738/) (2022)
- [Unified Multi-Modal Image Synthesis for Missing Modality Imputation](https://mlsanity.com/paper/2304.05340/) (2023)
- [Prototype Knowledge Distillation for Medical Segmentation with Missing Modality](https://mlsanity.com/paper/2303.09830/) (2023)
- [Representation Disentanglement for Multi-modal brain MR Analysis](https://mlsanity.com/paper/2102.11456/) (2021)
- [FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis](https://mlsanity.com/paper/2201.08953/) (2022)
- [Test-Time Training for Deformable Multi-Scale Image Registration](https://mlsanity.com/paper/2103.13578/) (2021)
- [SFusion: Self-attention based N-to-One Multimodal Fusion Block](https://mlsanity.com/paper/2208.12776/) (2022)
- [TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation](https://mlsanity.com/paper/2105.08993/) (2021)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective](https://mlsanity.com/paper/2207.06551/) (2022)
- [What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?](https://mlsanity.com/paper/2310.06383/) (2023)
- [DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2505.11921/) (2025)
- [fRegGAN with K-space Loss Regularization for Medical Image Translation](https://mlsanity.com/paper/2303.15938/) (2023)
- [CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation](https://mlsanity.com/paper/2309.03320/) (2023)
- [FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss](https://mlsanity.com/paper/2201.12589/) (2022)
- [A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data](https://mlsanity.com/paper/2312.05548/) (2023)
- [What can we learn about a generated image corrupting its latent representation?](https://mlsanity.com/paper/2210.06257/) (2022)
- [Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues](https://mlsanity.com/paper/2601.19750/) (2026)
- [MIST GAN: Modality Imputation Using Style Transfer for MRI](https://mlsanity.com/paper/2202.10396/) (2022)
- [Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning](https://mlsanity.com/paper/2208.12781/) (2022)
- [LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking](https://mlsanity.com/paper/2301.04748/) (2023)
- [K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment](https://mlsanity.com/paper/2307.04296/) (2023)
- [Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation](https://mlsanity.com/paper/2403.20168/) (2024)
- [Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation](https://mlsanity.com/paper/2408.08708/) (2024)
- [A Missing Data Imputation GAN for Character Sprite Generation](https://mlsanity.com/paper/2409.10721/) (2024)
- [Med-K2N: Flexible K-to-N Modality Translation for Medical Image Synthesis](https://mlsanity.com/paper/2510.02815/) (2025)
- [Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasets](https://mlsanity.com/paper/2602.19723/) (2026)
- [Segmentation-Assisted Brain MRI Synthesis with Cross-Image Multi-Contrast Feature Memory Bank Retrieval Augmentation](https://mlsanity.com/paper/2606.08421/) (2026)

## Abstract (arXiv)

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding and separate flesh encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.