Enhancing image classification models with multi-modal biomarkers

Jesus J. Caban; David Liao; Jianhua Yao; Daniel J. Mollura; Bernadette Gochuico; Terry Yoo

doi:10.1117/12.878084

8 March 2011 Enhancing image classification models with multi-modal biomarkers

Jesus J. Caban, David Liao, Jianhua Yao, Daniel J. Mollura, Bernadette Gochuico, Terry Yoo

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79631T (2011) https://doi.org/10.1117/12.878084
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Currently, most computer-aided diagnosis (CAD) systems rely on image analysis and statistical models to diagnose, quantify, and monitor the progression of a particular disease. In general, CAD systems have proven to be effective at providing quantitative measurements and assisting physicians during the decision-making process. As the need for more flexible and effective CADs continues to grow, questions about how to enhance their accuracy have surged. In this paper, we show how statistical image models can be augmented with multi-modal physiological values to create more robust, stable, and accurate CAD systems. In particular, this paper demonstrates how highly correlated blood and EKG features can be treated as biomarkers and used to enhance image classification models designed to automatically score subjects with pulmonary fibrosis. In our results, a 3-5% improvement was observed when comparing the accuracy of CADs that use multi-modal biomarkers with those that only used image features. Our results show that lab values such as Erythrocyte Sedimentation Rate and Fibrinogen, as well as EKG measurements such as QRS and I:40, are statistically significant and can provide valuable insights about the severity of the pulmonary fibrosis disease.

Citation Download Citation

Jesus J. Caban, David Liao, Jianhua Yao, Daniel J. Mollura, Bernadette Gochuico, and Terry Yoo "Enhancing image classification models with multi-modal biomarkers", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79631T (8 March 2011); https://doi.org/10.1117/12.878084

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