# Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection

arXiv: https://arxiv.org/abs/2111.00659 · HTML: https://mlsanity.com/paper/2111.00659/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 67 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Flowing ConvNets for Human Pose Estimation in Videos](https://mlsanity.com/paper/1506.02897/) (2015)
- [Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting](https://mlsanity.com/paper/1908.08841/) (2019)
- [An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms](https://mlsanity.com/paper/1906.07549/) (2019)
- [Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data](https://mlsanity.com/paper/1805.08687/) (2018)
- [Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression](https://mlsanity.com/paper/2012.12626/) (2020)

## Built on by (most cited first, 4 of 4)

- [Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection](https://mlsanity.com/paper/2410.04445/) (2024)
- [HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection](https://mlsanity.com/paper/2412.06499/) (2024)
- [Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection](https://mlsanity.com/paper/2507.02349/) (2025)
- [LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata](https://mlsanity.com/paper/2609.16033/) (2026)

## Abstract (arXiv)

Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. In this paper, we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks. To alleviate the problem of limited training data in the medical domain, our network adopts a deep network pre-trained on natural images as the backbone network and several popular networks have been compared. Our FARNet also includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate the end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. Our network has been evaluated on three publicly available anatomical landmark detection datasets, including cephalometric radiographs, hand radiographs, and spine radiographs, and achieves state-of-art performances on all three datasets. Code is available at: urlhttps://github.com/JuvenileInWind/FARNet