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Automatic detection and classification of lesions in medical images is a desirable goal, with numerous clinical applications. In breast imaging, multiple modalities such as X-ray, ultrasound and MRI are often used in the diagnostic workflow. Training robust classifiers for each modality is challenging due to the typically small size of the available datasets. We propose to use cross-modal transfer...
Radiomics, an emerging field of quantitative imaging, encompasses a broad class of analytical techniques. Recent literature have interrogated associations between quantitatively derived GLCM-based texture features and clinical/pathology information using machine learning algorithms in many cancer settings, but often fail to elucidate the predictive power of these features. Moreover, for many cancers...
The performance of smoothness-enforced Bayesian PET image reconstruction is strongly affected by the weight on regularization. Compromises need to be made between variance and spatial resolution. In this work, we propose to use an artificial neural network (ANN) to fuse the image versions reconstructed from a maximum a posteriori (MAP) algorithm with different regularizing weights for quantitative...
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