Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.

ABSTRACT:

Purpose

To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling.

Materials and methods

A total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pediatric hand radiographs with bone ages determined by a pediatric radiologist to develop models for bone age determination. The final results were determined using a test set of 200 radiographs labeled with the weighted average of six ratings. The mean pairwise model correlation and performance of all possible model combinations for ensembles of up to 10 models using the mean absolute deviation (MAD) were evaluated. A bootstrap analysis using the 200 test radiographs was conducted to e

SUBMITTER: Pan I 

PROVIDER: S-EPMC6884060 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature