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Stem Cell Plasticity and Niche Dynamics in Cancer Progression.

, , and . IEEE Trans. Biomed. Eng., 64 (3): 528-537 (2017)

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Stem Cell Plasticity and Niche Dynamics in Cancer Progression., , and . IEEE Trans. Biomed. Eng., 64 (3): 528-537 (2017)Exploring deep features from brain tumor magnetic resonance images via transfer learning., , , , , and . IJCNN, page 235-242. IEEE, (2016)Using features from tumor subregions of breast DCE-MRI for estrogen receptor status prediction., , , , , , and . SMC, page 2624-2629. IEEE, (2014)Identifying metastatic breast tumors using textural kinetic features of a contrast based habitat in DCE-MRI., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 941415. SPIE, (2015)Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97851H. SPIE, (2016)Aggregation Effects and Population-Based Dynamics as a Source of Therapy Resistance in Cancer., , and . IEEE Trans. Biomed. Eng., 64 (3): 512-518 (2017)Alternating synchronizability of complex clustered networks with regular local structure, , and . Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), (2008)A Texture Feature Ranking Model for Predicting Survival Time of Brain Tumor Patients., , , , and . SMC, page 4533-4538. IEEE, (2013)Texture Feature Analysis to Predict Metastatic and Necrotic Soft Tissue Sarcomas., , , , , and . SMC, page 2798-2802. IEEE, (2015)A quantitative histogram-based approach to predict treatment outcome for Soft Tissue Sarcomas using pre- and post-treatment MRIs., , , , , , and . SMC, page 4549-4554. IEEE, (2016)