# Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy

arXiv: https://arxiv.org/abs/2402.04566 · HTML: https://mlsanity.com/paper/2402.04566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2108.08829/) (2021)
- [DiffDP: Radiotherapy Dose Prediction via a Diffusion Model](https://mlsanity.com/paper/2307.09794/) (2023)
- [Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks](https://mlsanity.com/paper/1807.06489/) (2018)
- [A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning](https://mlsanity.com/paper/1709.09233/) (2017)
- [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023)

## Built on by (most cited first, 1 of 1)

- [ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer](https://mlsanity.com/paper/2408.13981/) (2024)

## Abstract (arXiv)

Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the radiotherapy plan-making by predicting the dose maps. However, current CNN-based methods ignore the remarkable dose difference in the dose map, i.e., high dose value in the interior PTV while low value in the exterior PTV, leading to a suboptimal prediction. In this paper, we propose a triplet-constraint transformer (TCtrans) with multi-scale refinement to predict the high-quality dose distribution. Concretely, a novel PTV-guided triplet constraint is designed to refine dose feature representations in the interior and exterior PTV by utilizing the explicit geometry of PTV. Furthermore, we introduce a multi-scale refinement (MSR) module to effectively fulfill the triplet constraint in different decoding layers with multiple scales. Besides, a transformer encoder is devised to learn the important global dosimetric knowledge. Experiments on a clinical cervical cancer dataset demonstrate the superiority of our method.