# CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection

arXiv: https://arxiv.org/abs/2511.10173 · HTML: https://mlsanity.com/paper/2511.10173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting](https://mlsanity.com/paper/1908.08841/) (2019)
- [CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion](https://mlsanity.com/paper/2308.01239/) (2023)
- [MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation](https://mlsanity.com/paper/2509.18493/) (2025)
- [Cephalometric Landmark Detection across Ages with Prototypical Network](https://mlsanity.com/paper/2406.12577/) (2024)
- [On the Separability of Classes with the Cross-Entropy Loss Function](https://mlsanity.com/paper/1909.06930/) (2019)
- [Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head](https://mlsanity.com/paper/2309.17143/) (2023)
- ['Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification](https://mlsanity.com/paper/2302.07797/) (2023)

## Abstract (arXiv)

Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis.