# Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images

arXiv: https://arxiv.org/abs/2309.16066 · HTML: https://mlsanity.com/paper/2309.16066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)

## Built on by (most cited first, 2 of 2)

- [2D/3D Registration of Acetabular Hip Implants Under Perspective Projection and Fully Differentiable Ellipse Fitting](https://mlsanity.com/paper/2503.07763/) (2025)
- [Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss](https://mlsanity.com/paper/2511.21575/) (2025)

## Abstract (arXiv)

This work reports the empirical performance of an automated medical landmark detection method for predict clinical markers in hip radiograph images. Notably, the detection method was trained using a label-only augmentation scheme; our results indicate that this form of augmentation outperforms traditional data augmentation and produces highly sample efficient estimators. We train a generic U-Net-based architecture under a curriculum consisting of two phases: initially relaxing the landmarking task by enlarging the label points to regions, then gradually eroding these label regions back to the base task. We measure the benefits of this approach on six datasets of radiographs with gold-standard expert annotations.