Characterization of diffusion-weighted imaging signal is typically performed by modeling the data based on biophysical, mathematical, and/or statistical models to estimate quantitative biomarkers. However, conventional nonlinear fitting, which is required for the estimation of model parameters, often suffers from instability and degeneracy. In this study, we propose a Model-free Diffusion-wEighted MRI technique (MODEM) with machine learning to detect cervical carcinomas by using diffusion signal intensities and the first-order statistical features extracted from the signal attenuation as the input. By using MODEM, superior diagnostic performance and stability can be achieved even with limited number of b-values in cervical cancer detection.
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