# CollaGAN : Collaborative GAN for Missing Image Data Imputation

arXiv: https://arxiv.org/abs/1901.09764 · HTML: https://mlsanity.com/paper/1901.09764/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 30 of 33)

- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [SAITS: Self-Attention-based Imputation for Time Series](https://mlsanity.com/paper/2202.08516/) (2022)
- [Diffusion-based Image Translation using Disentangled Style and Content Representation](https://mlsanity.com/paper/2209.15264/) (2022)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [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)
- [Self-Consistent Recursive Diffusion Bridge for Medical Image Translation](https://mlsanity.com/paper/2405.06789/) (2024)
- [I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling](https://mlsanity.com/paper/2405.14022/) (2024)
- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [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)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems](https://mlsanity.com/paper/2112.11507/) (2021)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations](https://mlsanity.com/paper/2309.08747/) (2023)
- [Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts](https://mlsanity.com/paper/2410.19378/) (2024)
- [Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks](https://mlsanity.com/paper/2105.10325/) (2021)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction](https://mlsanity.com/paper/2203.04692/) (2022)
- [Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis](https://mlsanity.com/paper/2411.14684/) (2024)
- [Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging](https://mlsanity.com/paper/2109.09004/) (2021)
- [IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance](https://mlsanity.com/paper/2110.07460/) (2021)
- [Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data](https://mlsanity.com/paper/2211.13297/) (2022)
- [Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Curriculum Data Erasing Guided Knowledge Distillation](https://mlsanity.com/paper/2407.19540/) (2024)
- [Multilevel Stochastic Optimization for Imputation in Massive Medical Data Records](https://mlsanity.com/paper/2110.09680/) (2021)
- [A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data](https://mlsanity.com/paper/2312.05548/) (2023)
- [Supplementing Missing Visions via Dialog for Scene Graph Generations](https://mlsanity.com/paper/2204.11143/) (2022)
- [Facing the Void: Overcoming Missing Data in Multi-View Imagery](https://mlsanity.com/paper/2205.10592/) (2022)
- [BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data](https://mlsanity.com/paper/2402.18800/) (2024)

## Abstract (arXiv)

In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias. Although many techniques have been developed for imputing missing data, the image imputation is still difficult due to complicated nature of natural images. To address this problem, here we proposed a novel framework for missing image data imputation, called Collaborative Generative Adversarial Network (CollaGAN). CollaGAN converts an image imputation problem to a multi-domain images-to-image translation task so that a single generator and discriminator network can successfully estimate the missing data using the remaining clean data set. We demonstrate that CollaGAN produces the images with a higher visual quality compared to the existing competing approaches in various image imputation tasks.