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Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data.

, , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9788 of SPIE Proceedings, page 978811. SPIE, (2016)

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Research and applications: Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy., , , , , , , , , and . JAMIA, 20 (4): 688-695 (2013)Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data., , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9788 of SPIE Proceedings, page 978811. SPIE, (2016)Towards quantitative quasi-static elastography with a gravity-induced deformation source., , , , and . Medical Imaging: Image-Guided Procedures, volume 10135 of SPIE Proceedings, page 1013502. SPIE, (2017)Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI., , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 10 (12): 1985-1996 (2015)Curvelets as a sparse basis for compressed sensing magnetic resonance imaging., , , and . Medical Imaging: Image Processing, volume 8669 of SPIE Proceedings, page 866929. SPIE, (2013)Early DCE-MRI Changes after Longitudinal Registration May Predict Breast Cancer Response to Neoadjuvant Chemotherapy., , , , , , , , , and . WBIR, volume 7359 of Lecture Notes in Computer Science, page 229-235. Springer, (2012)Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI., , , , , , and . IEEE Trans. Med. Imaging, 31 (2): 504-511 (2012)Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast., , , , and . Int. J. Biomedical Imaging, (2017)