# Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging

arXiv: https://arxiv.org/abs/2109.09004 · HTML: https://mlsanity.com/paper/2109.09004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [CollaGAN : Collaborative GAN for Missing Image Data Imputation](https://mlsanity.com/paper/1901.09764/) (2019)
- [Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth](https://mlsanity.com/paper/1712.07695/) (2017)
- [Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue](https://mlsanity.com/paper/2001.07267/) (2020)
- [Unsupervised Histopathology Image Synthesis](https://mlsanity.com/paper/1712.05021/) (2017)
- [Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks](https://mlsanity.com/paper/1811.04045/) (2018)
- [Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets](https://mlsanity.com/paper/1901.11369/) (2019)
- [Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer](https://mlsanity.com/paper/1906.00617/) (2019)

## Built on by (most cited first, 3 of 3)

- [Expanding Small-Scale Datasets with Guided Imagination](https://mlsanity.com/paper/2211.13976/) (2022)
- [A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels](https://mlsanity.com/paper/2205.10373/) (2022)
- [MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models](https://mlsanity.com/paper/2505.10294/) (2025)

## Abstract (arXiv)

Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing', MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ('missing tissue'). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ('missing stain''). In this work, we focus on the 'missing stain' issue. It would be appealing to develop digital image synthesis approaches to restore missing stain images without losing more tissue physically. Herein, we aim to develop image synthesis approaches for eleven MxIF structural molecular markers (i.e., epithelial and stromal) on real samples. We propose a novel multi-channel high-resolution image synthesis approach, called pixN2N-HD, to tackle possible missing stain scenarios via a high-resolution generative adversarial network (GAN). Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e.g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF. Our results elucidate a promising direction of advancing MxIF imaging with deep image synthesis.