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Bone age assessment is challenging in clinical practice due to thecomplicated bone age assessment process. Current automatic bone age assessmentmethods were designed with rare consideration of the diagnostic logistics andthus may yield certain uninterpretable hidden states and outputs. Consequently,doctors can find it hard to cooperate with such models harmoniously because itis difficult to check the correctness of the model predictions. In this work,we propose a new graph-based deep learning framework for bone age assessmentwith hand radiographs, called Doctor Imitator (DI). The architecture of DI isdesigned to learn the diagnostic logistics of doctors using the scoring methods(e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, theconvolutions of DI capture the local features of the anatomical regions ofinterest (ROIs) on hand radiographs and predict the ROI scores by our proposedAnatomy-based Group Convolution, summing up for bone age prediction. Besides,we develop a novel Dual Graph-based Attention module to computepatient-specific attention for ROI features and context attention for ROIscores. As far as we know, DI is the first automatic bone age assessmentframework following the scoring methods without fully supervised handradiographs. Experiments on hand radiographs with only bone age supervisionverify that DI can achieve excellent performance with sparse parameters andprovide more interpretability.

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods