Uncertainty for Proximal Femur Fractures Classification

Selina Frenner, Mayar Lotfy, Marc Beirer, Peter Biberthaler, Shadi Albarqouni

Keywords: Deep Learning, Uncertainty, Quality Control, Radiology, Proximal femur fractures

TL;DR: Uncertainty score using Monte Carlo dropout layers as a quality control for automated proximal femur fracture classification using the AO-fracture classification system.

Abstract:

Deep Learning methods over the past years provided high-performance solutions for the
medical applications. Yet, robustness and quality control is still required for clinical applicability.
In this work, the uncertainty of proximal femur fracture classification,was modeled.
We introduce a reliability measure to our predictive model using the Monte Carlo Dropout
approach. We performed an extensive quantitative and qualitative analysis to validate the
results. We further exposed the results to expert physicians in order to get feedback on the
model’s performance and uncertainty measures. Results demonstrate a positive correlation
between the miss-classification of the model’s prediction and high uncertainty scores. Additionally,
the uncertainty measures are mimicking the actual radiologist’s uncertainty for
challenging examples reflected on intra- and inter- experts variability.