Anatomical Priors for Weakly Supervised Osteophyte Detection and Localization in Hip X-rays

Abstract

Osteophytes, bony projections associated with Osteoarthritis, are traditionally identified through time-consuming and subjective manual X-ray assessment. While deep learning approaches have shown promising results in medical image analysis, relatively few methods are designed to detect the presence and localization of osteophytes, particularly in settings where only image-level labels are available and precise pixel-level annotations are missing.

This work investigates whether anatomical priors derived from landmark points can improve weakly supervised osteophyte detection and localization in hip X-rays when only image-level labels are available. We propose modified ResNet-18 architectures that integrate anatomical guidance to highlight likely osteophyte regions.

We evaluate the proposed models across varying training data sizes. The results show that models with anatomical guidance generally outperform baseline models, with the most consistent improvements observed in classification metrics, while localization results are less conclusive. Additionally, experiments performed without guidance during testing led to reduced classification performance. Overall, the results suggest that anatomical priors provide useful complementary information for weakly supervised osteophyte detection, although they do not fully compensate for limited training data. Moreover, the benefit of guidance information varies across architectures and training set sizes.