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Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy.

, , , , , and . IEEE Trans. Med. Imaging, 33 (6): 1390-1400 (2014)

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Low-frequency ultrasound radiosensitization and therapy response monitoring of tumors: An in vivo study., , , , and . EMBC, page 3227-3230. IEEE, (2016)Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers*., , , , , and . EMBC, page 1250-1253. IEEE, (2020)Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response., , and . EMBC, page 2672-2675. IEEE, (2012)Cancer therapy prognosis using quantitative ultrasound spectroscopy and a kernel-based metric., , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 903406. SPIE, (2014)Assessment of cancer therapy effects using texton-based characterization of quantitative ultrasound parametric images., , , and . ISBI, page 1372-1375. IEEE, (2013)Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response., , and . EMBC, page 1125-1128. IEEE, (2012)Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy., , , , , and . IEEE Trans. Med. Imaging, 33 (6): 1390-1400 (2014)Quantitative ultrasound spectroscopy and a kernel-based metric in clinical cancer response monitoring., , , , and . ISBI, page 255-259. IEEE, (2015)Advanced machine learning and textural methods in monitoring cell death using quantitative ultrasound spectroscopy., , , , and . ISBI, page 646-650. IEEE, (2015)Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer., , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 978413. SPIE, (2016)