Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity

Abstract

The effects of label noise and ambiguity are widespread, especially for subjective tasks such as bone age assessment (BAA). However, most existing BAA algorithms ignore these issues. We propose a robust framework for BAA supporting Tanner & Whitehouse 3 (TW3) method, which is clinically more objective and reproducible than Greulich & Pyle (GP) method, but has received less attention from the research community. Since the publicly available RSNA BAA dataset was annotated using GP method, we contribute additional TW3 annotations. We formulate TW3 BAA as an ordinal regression problem, and address both label noise and ambiguity with a two stage deep learning framework. The first stage focuses on correcting erroneous labels with ambiguity tolerated, while the latter stage introduces a module called Residual Context Graph (RCG) to conquer label ambiguity. Inspired by the way human experts handle ambiguity, we combine fine-grained local features with a graph based context. Experiments show the proposed framework outperforms previously reported TW3-based BAA systems by large margins. TW3 annotations of bone maturity levels for a portion of the RSNA BAA dataset will be made publicly available.