1. Improving Model's Interpretability and Reliability using Biomarkers
- Author
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Gare, Gautam Rajendrakumar, Fox, Tom, Chansangavej, Beam, Krishnan, Amita, Rodriguez, Ricardo Luis, deBoisblanc, Bennett P, Ramanan, Deva Kannan, and Galeotti, John Michael
- Subjects
Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
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., Comment: Accepted at BIAS 2023 Conference
- Published
- 2024