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

TL;DR

This paper introduces Doctor Imitator, a graph-based deep learning framework that mimics doctors' scoring methods for bone age assessment using hand radiographs, enhancing interpretability and performance.

Contribution

It proposes a novel architecture that learns diagnostic logistics of doctors' scoring methods and incorporates attention mechanisms for improved bone age prediction.

Findings

Achieves high accuracy with sparse parameters

Provides better interpretability of predictions

Works effectively with limited supervision

Abstract

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