In this paper, a multi-stage ensemble learning based on the majority voting mechanism was designed to leverage the contradiction between an insufficient number of thyroid MRI and well-trained deep learning models that accurately predicted the pathology of thyroid micronodules. And its clinical applicability value was also assessed in terms of micronodule risk stratification and optimal regimen selection on high b-value (2000 s/mm2) diffusion-weighted images. Experimental results proved that our model had the capability of effectively distinguishing benign and malignant micronodules on small-dataset thyroid DWI images.
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