Evaluating Baseline and Anatomically Guided Preprocessing for Weakly Supervised Hip Osteophyte Classification

Abstract

Weakly supervised osteophyte classification in hip X-ray images is challenging because only image-level labels are available, providing no explicit information about osteophyte location. However, anatomical landmarks can be used to identify regions where osteophytes are most likely to occur and guide the model towards clinically relevant structures. At the same time, broader anatomical context may also contain useful information for classification. As a result, it remains unclear whether models benefit more from broad anatomical context or from localized regions centered on anatomically relevant structures. This project evaluates whether anatomically guided preprocessing can improve weakly supervised hip osteophyte classification compared to a baseline preprocessing approach. Hip X-rays from the Osteoarthritis Initiative (OAI) and CHECK datasets were processed using two strategies: broad femoral head centered crops and localized landmark based crops generated using BoneFinder anatomical landmarks. ResNet-18 models were trained for binary osteophyte classification and evaluated using ROC-AUC. We further hypothesized that anatomically guided preprocessing would be particularly beneficial when training data is limited, as focusing on clinically relevant regions may improve data efficiency. To investigate this, additional experiments were conducted using reduced training set sizes (50%, 25%, and 10% of the available training data). Unexpectedly, the results show that the baseline preprocessing approach consistently achieved higher classification performance than the anatomically guided approach across all evaluated anatomical regions, despite using lower resolution crops than the landmark-guided approach. For example, the baseline model achieved an ROC-AUC of 0.889 for superior femoral osteophyte classification, whereas the corresponding landmark-based model achieved an ROC-AUC of 0.783. Reducing the training set size generally reduced performance for both approaches. These findings suggest that localized landmark based crops do not necessarily improve weakly supervised osteophyte classification and that broader anatomical context may provide important information to predict accurately. Future work could investigate alternative localization strategies and more precise osteophyte annotations. The source code used in this study is publicly available at: https://github.com/egeyarar/osteophyte-classification