Improving Model's Interpretability and Reliability using Biomarkers
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916589430898688 |
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| author | Gare, Gautam Rajendrakumar Fox, Tom Chansangavej, Beam Krishnan, Amita Rodriguez, Ricardo Luis deBoisblanc, Bennett P Ramanan, Deva Kannan Galeotti, John Michael |
| author_facet | Gare, Gautam Rajendrakumar Fox, Tom Chansangavej, Beam Krishnan, Amita Rodriguez, Ricardo Luis deBoisblanc, Bennett P Ramanan, Deva Kannan Galeotti, John Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Model's Interpretability and Reliability using Biomarkers Gare, Gautam Rajendrakumar Fox, Tom Chansangavej, Beam Krishnan, Amita Rodriguez, Ricardo Luis deBoisblanc, Bennett P Ramanan, Deva Kannan Galeotti, John Michael Human-Computer Interaction Artificial Intelligence Machine Learning Image and Video Processing 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. |
| title | Improving Model's Interpretability and Reliability using Biomarkers |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2402.12394 |