SIMBA: Specific Identity Markers for Bone Age Assessment

TL;DR

SIMBA introduces a novel method for bone age assessment that integrates patient identity markers like age and gender with radiograph features, outperforming existing models and promoting comprehensive data utilization in diagnosis.

Contribution

The paper presents SIMBA, a new approach that fuses identity markers with visual features for improved bone age estimation, advancing automated diagnostic methods.

Findings

Outperforms previous state-of-the-art methods on the Radiological Hand Pose Estimation dataset.

Demonstrates the effectiveness of incorporating identity markers in bone age assessment.

Provides source code and pre-trained models to facilitate further research.

Abstract

Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity markers when calculating bone age, i.e., chronological age and gender. However, the current automated Bone Age Assessment methods do not completely exploit the information present in the patient's metadata. With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment. SIMBA is a novel approach for the task of BAA based on the use of identity markers. For this purpose, we build upon the state-of-the-art model, fusing the information present in the identity markers with the visual features created from the original hand radiograph. We then use this robust representation to estimate the patient's relative bone age: the difference between chronological age and bone age. We validate SIMBA on the Radiological Hand Pose Estimation dataset and find that it outperforms previous state-of-the-art methods. SIMBA sets a trend of a new wave of Computer-aided Diagnosis methods that incorporate all of the data that is available regarding a patient. To promote further research in this area and ensure reproducibility we will provide the source code as well as the pre-trained models of SIMBA.