Learning rich attention for pediatric bone age assessment

Abstract

Bone Age Assessment (BAA) is a challenging clinical practice in pediatrics, which requires rich attention on multiple anatomical Regions of Interest (RoIs). Recently developed deep learning methods address the challenge in BAA with a hard-crop attention mechanism, which segments or detects the discriminative RoIs for meticulous analysis. Great strides have been made, however, these methods face severe requirements on precise RoIs annotation, complex network design and expensive computing expenditure. In this paper, we show it is possible to learn rich attention without the need for complicated network design or precise annotation – a simple module is all it takes. The proposed Rich Attention Network (RA-Net) is composed of a flexible baseline network and a lightweight Rich Attention module (RAm). Taking the feature map from baseline network, the RA module is optimized to generate attention with discriminability and diversity, thus the deep network can learn rich pattern attention and representation. With this artful design, we enable an end-to-end framework for BAA without RoI annotation. The RA-Net brings significant margin in performance, meanwhile negligible additional overhead in parameter and computation. Extensive experiments verify that our method yields state-of-the-art performance on the public RSNA datasets with mean absolute error (MAE) of 4.10 months.