# AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation

arXiv: https://arxiv.org/abs/2501.12840 · HTML: https://mlsanity.com/paper/2501.12840/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Cross-conditioned Diffusion Model for Medical Image to Image Translation](https://mlsanity.com/paper/2409.08500/) (2024)
- [A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion](https://mlsanity.com/paper/2207.03430/) (2022)

## Built on by (most cited first, 6 of 6)

- [How Far Are We from Generating Missing Modalities with Foundation Models?](https://mlsanity.com/paper/2506.03530/) (2025)
- [Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression](https://mlsanity.com/paper/2509.10824/) (2025)
- [Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration](https://mlsanity.com/paper/2602.03151/) (2026)
- [AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference](https://mlsanity.com/paper/2606.11186/) (2026)
- [Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions](https://mlsanity.com/paper/2607.21904/) (2026)
- [VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection](https://mlsanity.com/paper/2609.05537/) (2026)

## Abstract (arXiv)

In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing MR modalities pose significant challenges for tasks like brain tumor segmentation, especially in deep learning-based segmentation, as each modality provides complementary information crucial for improving accuracy. A promising solution is missing data imputation, where absent modalities are generated from available ones. While generative models have been widely used for this purpose, most state-of-the-art approaches are limited to single or dual target translations, lacking the adaptability to generate missing modalities based on varying input configurations. To address this, we propose an Adaptive Multi-Modality Diffusion Network (AMM-Diff), a novel diffusion-based generative model capable of handling any number of input modalities and generating the missing ones. We designed an Image-Frequency Fusion Network (IFFN) that learns a unified feature representation through a self-supervised pretext task across the full input modalities and their selected high-frequency Fourier components. The proposed diffusion model leverages this representation, encapsulating prior knowledge of the complete modalities, and combines it with an adaptive reconstruction strategy to achieve missing modality completion. Experimental results on the BraTS 2021 dataset demonstrate the effectiveness of our approach.