# Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection

arXiv: https://arxiv.org/abs/2410.04445 · HTML: https://mlsanity.com/paper/2410.04445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform](https://mlsanity.com/paper/2110.05802/) (2021)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [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)
- [Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection](https://mlsanity.com/paper/2111.00659/) (2021)
- [Uncertainty-aware U-Net for Medical Landmark Detection](https://mlsanity.com/paper/2303.10349/) (2023)

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

- [CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection](https://mlsanity.com/paper/2606.04898/) (2026)

## Abstract (arXiv)

Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown.