# Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction

arXiv: https://arxiv.org/abs/2508.02043 · HTML: https://mlsanity.com/paper/2508.02043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Placed in

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

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging](https://mlsanity.com/paper/2406.05285/) (2024)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model](https://mlsanity.com/paper/2502.09533/) (2025)
- [Simplifying Transformer Blocks](https://mlsanity.com/paper/2311.01906/) (2023)
- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [IMAGHarmony: Controllable Image Editing with Consistent Object Quantity and Layout](https://mlsanity.com/paper/2506.01949/) (2025)
- [3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces](https://mlsanity.com/paper/2111.12448/) (2021)
- [MD-Dose: A diffusion model based on the Mamba for radiation dose prediction](https://mlsanity.com/paper/2403.08479/) (2024)
- [Convolutional neural networks for medical image segmentation](https://mlsanity.com/paper/2211.09562/) (2022)

## Abstract (arXiv)

Radiotherapy treatment planning often relies on time-consuming, trial-and-error adjustments that heavily depend on the expertise of specialists, while existing deep learning methods face limitations in generalization, prediction accuracy, and clinical applicability. To tackle these challenges, we propose ADDiff-Dose, an Anatomical-Dose Dual Constraints Conditional Diffusion Model for end-to-end multi-tumor dose prediction. The model employs LightweightVAE3D to compress high-dimensional CT data and integrates multimodal inputs, including target and organ-at-risk (OAR) masks and beam parameters, within a progressive noise addition and denoising framework. It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence to ensure both dosimetric accuracy and compliance with clinical constraints. Evaluation on a large-scale public dataset (2,877 cases) and three external institutional cohorts (450 cases in total) demonstrates that ADDiff-Dose significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154 (compared to 0.316 for UNet and 0.169 for GAN models), a DICE coefficient of 0.927 (a 6.8% improvement), and limiting spinal cord maximum dose error to within 0.1 Gy. The average plan generation time per case is reduced to 22 seconds. Ablation studies confirm that the structural encoder enhances compliance with clinical dose constraints by 28.5%. To our knowledge, this is the first study to introduce a conditional diffusion model framework for radiotherapy dose prediction, offering a generalizable and efficient solution for automated treatment planning across diverse tumor sites, with the potential to substantially reduce planning time and improve clinical workflow efficiency.