# Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review

arXiv: https://arxiv.org/abs/2004.10322 · HTML: https://mlsanity.com/paper/2004.10322/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

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

- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis](https://mlsanity.com/paper/2405.05667/) (2024)
- [Diffeomorphism-Equivariant Neural Networks](https://mlsanity.com/paper/2602.06695/) (2026)

## Abstract (arXiv)

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were summarized in the discussion part.