Towards Achieving Diagnostic Consensus in Medical Image Interpretation

Abstract

The workload associated with the daily job of a clinical radiologist has been steadily increasing as the volume of the archived and the newly acquired images grows. Computer-aided diagnostic systems are becoming an indispensable tool in automating image analysis and providing preliminary diagnosis that can help guide radiologist's decisions. In this paper, we introduce a novel metric to evaluate the difficulty of reaching diagnostic consensus when interpreting a case and illustrate several benefits that such insight can provide. Using a lung nodule image dataset, we demonstrate how a metric-based case partitioning can be used to better select how many radiologists are assigned to each case and how to identify image features that provide important feedback to further assist with the diagnosis. This knowledge can also be leveraged to shed 25% of radiologist annotations without any loss in predictive accuracy.