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Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions.

H. Whitney, Y. Ji, H. Li, A. Edwards, J. Papaioannou, P. Liu, and M. Giger. Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109501A. SPIE, (2019)

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Whitney Frazier Peterson University of Stuttgart

M. Fittingho

M. Nudischer

M. Kratzer

M. Liewald

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Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.Q. Hu, H. Whitney, and M. Giger. CoRR, (2019)Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer.Q. Hu, H. Whitney, A. Edwards, J. Papaioannou, and M. Giger. Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109504A. SPIE, (2019)Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.H. Whitney, K. Drukker, A. Edwards, J. Papaioannou, and M. Giger. IWBI, volume 10718 of SPIE Proceedings, page 107180H. SPIE, (2018)Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths.H. Whitney, K. Drukker, A. Edwards, J. Papaioannou, and M. Giger. Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750A. SPIE, (2018)Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method.H. Whitney, H. Li, Y. Ji, P. Liu, and M. Giger. Proceedings of the IEEE, 108 (1): 163-177 (2020)Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions.H. Whitney, Y. Ji, H. Li, A. Edwards, J. Papaioannou, P. Liu, and M. Giger. Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109501A. SPIE, (2019)