Improving Model's Interpretability and Reliability using Biomarkers
Gautam Rajendrakumar Gare Tom Fox Beam Chansangavej Amita Krishnan Ricardo Luis Rodriguez Bennett P deBoisblanc Deva Kannan Ramanan John Michael Galeotti
Accurate and interpretable diagnostic models are crucial in the
safety-critical field of medicine. We investigate the interpretability of our
proposed biomarker-based lung ultrasound diagnostic pipeline to enhance
clinicians' diagnostic capabilities. The objective of this study is to assess
whether explanations from a decision tree classifier, utilizing biomarkers, can
improve users' ability to identify inaccurate model predictions compared to
conventional saliency maps. Our findings demonstrate that decision tree
explanations, based on clinically established biomarkers, can assist clinicians
in detecting false positives, thus improving the reliability of diagnostic
models in medicine.