# DATR: Domain-adaptive transformer for multi-domain landmark detection

arXiv: https://arxiv.org/abs/2203.06433 · HTML: https://mlsanity.com/paper/2203.06433/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [You Only Learn Once: Universal Anatomical Landmark Detection](https://mlsanity.com/paper/2103.04657/) (2021)
- [Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection](https://mlsanity.com/paper/2007.05225/) (2020)
- [A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation](https://mlsanity.com/paper/2106.07557/) (2021)
- [Hybrid graph convolutional neural networks for landmark-based anatomical segmentation](https://mlsanity.com/paper/2106.09832/) (2021)

## Built on by (most cited first, 8 of 8)

- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference](https://mlsanity.com/paper/2306.13988/) (2023)
- [H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging](https://mlsanity.com/paper/2502.14221/) (2025)
- [Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images](https://mlsanity.com/paper/2307.03535/) (2023)
- [HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection](https://mlsanity.com/paper/2412.06499/) (2024)
- [Coordinated Transformer with Position & Sample-aware Central Loss for Anatomical Landmark Detection](https://mlsanity.com/paper/2305.11338/) (2023)
- [UAE: Universal Anatomical Embedding on Multi-modality Medical Images](https://mlsanity.com/paper/2311.15111/) (2023)
- [Adaptive Query Prompting for Multi-Domain Landmark Detection](https://mlsanity.com/paper/2404.01194/) (2024)

## Abstract (arXiv)

Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based on CNN and specialized for a single domain say associated with a particular anatomical region. In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies. The proposed DATR exhibits three primary features: (i) It is the first universal model which introduces transformer as an encoder for multi-anatomy landmark detection; (ii) We design a domain-adaptive transformer for anatomy-aware landmark detection, which can be effectively extended to any other transformer network; (iii) Following previous studies, we employ a light-weighted guidance network, which encourages the transformer network to detect more accurate landmarks. We carry out experiments on three widely used X-ray datasets for landmark detection, which have 1,588 images and 62 landmarks in total, including three different anatomies (head, hand, and chest). Experimental results demonstrate that our proposed DATR achieves state-of-the-art performances by most metrics and behaves much better than any previous convolution-based models. The code will be released publicly.