Improving Model's Interpretability and Reliability using Biomarkers
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticSimilar Papers 제목 키워드 기반
Reliability and validity of TMS-EEG biomarkers
Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience, with a multitude of applications including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore part…
DiagnosticEEGElectroencephalogram (EEG)Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning
Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identif…
DiagnosticDrug DiscoveryUnlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretabil…
Alzheimer's DetectionDiagnosticMIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning
Detecting microbial biomarkers used to predict disease phenotypes and clinical outcomes is crucial for disease early-stage screening and diagnosis. Most methods for biomarker identification are linear-based, which is ver…
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models
Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their practical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical ch…
Diagnosticfeature selectionInterpretable Machine LearningPrognosis