Landmark-Based Anatomical Priors as Penalty Masks in Weakly Supervised Learning

Abstract

Weakly supervised learning can reduce the annotation burden for radiographic osteophyte detection because models can be trained with image-level labels rather than pixel-level masks. However, image-level supervision does not specify where the pathology is located, and a classifier may therefore base its decisions on irrelevant anatomical regions. This paper studies whether landmark-based anatomical priors can improve the classification performance and spatial behaviour of a weakly supervised hip osteophyte classifier. Using the CHECK and OAI datasets, we train a ResNet-18 baseline to predict four binary osteophyte targets and compare it with a prior-guided model that adds a penalty to class activation maps during training. The penalty is constructed from BoneFinder landmarks and uses a plateau Gaussian mask around four anatomical target zones. Performance is evaluated using AUC, heatmap centre-of-mass distance, peak distance, spread, paired Wilcoxon signed-rank tests, and qualitative heatmap visualizations. The prior-guided model produces more compact heatmaps that are significantly closer to the landmark-defined anatomical target zones, with mean centre-of-mass distance reductions between 23.9 and 74.0 pixels, mean peak distance reductions between 24.9 and 73.1 pixels, and spread reductions between 24.5 and 33.7 when evaluated on positive osteophyte cases only. Classification performance remains similar to the baseline, with AUC differences between -0.01 and +0.01. These findings indicate that landmark-based penalty masks can improve alignment of class-discriminative heatmaps in weakly supervised hip osteophyte detection without requiring pixel-level osteophyte annotations.