arXiv Open Access 2024

Improving Model's Interpretability and Reliability using Biomarkers

Gautam Rajendrakumar Gare Tom Fox Beam Chansangavej Amita Krishnan Ricardo Luis Rodriguez +3 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.HC cs.AI cs.LG eess.IV

Penulis (8)

Gautam Rajendrakumar Gare

Tom Fox

Beam Chansangavej

Amita Krishnan

Ricardo Luis Rodriguez

Bennett P deBoisblanc

Deva Kannan Ramanan

John Michael Galeotti

Format Sitasi

APA MLA BibTeX

Gare, G.R., Fox, T., Chansangavej, B., Krishnan, A., Rodriguez, R.L., deBoisblanc, B.P. et al. (2024). Improving Model's Interpretability and Reliability using Biomarkers. https://arxiv.org/abs/2402.12394

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓