Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images
This work addresses the challenge of improving landmark detection accuracy for clinical applications in medical imaging, though it appears incremental as it builds on existing U-Net architectures.
The paper tackled the problem of automated medical landmark detection in hip radiograph images by introducing a label-only augmentation scheme, which outperformed traditional data augmentation and achieved high sample efficiency, as demonstrated on six datasets with expert annotations.
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.