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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)Spectral sparsification in spectral clustering., , and . ICPR, page 2301-2306. IEEE, (2016)Random eye state change detection in real-time using EEG signals., , , and . Expert Syst. Appl., (2017)Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850T. SPIE, (2016)Prediction of treatment response and metastatic disease in soft tissue sarcoma., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 903518. SPIE, (2014)Probabilistic Neural Network Training for Semi-Supervised Classifiers.. CoRR, (2015)Prediction of treatment outcome in soft tissue sarcoma based on radiologically defined habitats., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94141U. SPIE, (2015)Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI.. University of South Florida, Tampa, USA, (2017)base-search.net(ftunisfloridatam:oai:digitalcommons.usf.edu:etd-8595).Classification of progression free survival with nasopharyngeal carcinoma tumors., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97851I. SPIE, (2016)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)