Search Results - "Liu, Tiffany Ting"

Lymphedema microsurgery improved outcomes of pediatric primary extremity lymphedema by Cheng, Ming‐Huei, Liu, Tiffany Ting‐Fong

“…Background Primary lymphedema is an anomaly of the regional lymphatic system with long symptom duration or severe lymphatic obstruction. Few microsurgical…”
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Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions by Choi, Yoonha, Liu, Tiffany Ting, Pankratz, Daniel G, Colby, Thomas V, Barth, Neil M, Lynch, David A, Walsh, P Sean, Raghu, Ganesh, Kennedy, Giulia C, Huang, Jing

“…We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary…”
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Revealing cancer subtypes with higher-order correlations applied to imaging and omics data by Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, 4th, Griffith R, Cordero, Sergio P, Rubin, Daniel L, Stuart, Joshua M

“…Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis…”
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Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling by Chenevert, Thomas L, Malyarenko, Dariya I, Newitt, David, Li, Xin, Jayatilake, Mohan, Tudorica, Alina, Fedorov, Andriy, Kikinis, Ron, Liu, Tiffany Ting, Muzi, Mark, Oborski, Matthew J, Laymon, Charles M, Li, Xia, Thomas, Yankeelov, Jayashree, Kalpathy-Cramer, Mountz, James M, Kinahan, Paul E, Rubin, Daniel L, Fennessy, Fiona, Huang, Wei, Hylton, Nola, Ross, Brian D

“…Abstract PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image…”
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Abstract: Lymphedema Microsurgery Improved the Outcomes of Congenital Limb Lymphedema by Ting-Fong Liu, Tiffany, Cheng, Ming-Huei

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Deep Learning Approaches Substantially Improve Automated Extraction of Information from Free-Text Medical Reports by Liu, Tiffany Ting

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Primary Lymphedema with Concomitant Vascular and Lymphatic Lesions by Cheng, Ming-Huei, Liu, Tiffany Ting-Fong

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NIMG-27. GLIOBLASTOMA TUMOR SEGMENTATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS by Liu, Tiffany Ting, Achrol, Achal, Rubin, Daniel, Chang, Steven

“…Glioblastoma (GBM), the most malignant primary brain cancer, is mainly diagnosed initially based on tumor appearance on magnetic resonance (MR) images…”
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Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers by Buckler, Andrew J., Ouellette, M., Danagoulian, J., Wernsing, G., Liu, Tiffany Ting, Savig, Erica, Suzek, Baris E., Rubin, Daniel L., Paik, David

“…A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have…”
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Integrated Analysis Using Molecular-Scale and Imaging-Scale Data to Identify Prognostic Subgroups in Glioblastoma by Liu, Tiffany Ting

“…Glioblastoma (GBM, World Health Organization [WHO] grade IV) is the most common and most aggressive brain cancer in adults, with a median survival of…”
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Erratum to: Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers by Buckler, Andrew J., Liu, Tiffany Ting, Savig, Erica, Suzek, Baris E., Rubin, Daniel L., Paik, David

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Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling1 by Chenevert, Thomas L, Malyarenko, Dariya I, Newitt, David, Li, Xin, Jayatilake, Mohan, Tudorica, Alina, Fedorov, Andriy, Kikinis, Ron, Liu, Tiffany Ting, Muzi, Mark, Oborski, Matthew J, Laymon, Charles M, Li, Xia, Thomas, Yankeelov, Jayashree, Kalpathy-Cramer, Mountz, James M, Kinahan, Paul E, Rubin, Daniel L, Fennessy, Fiona, Huang, Wei, Hylton, Nola, Ross, Brian D

“…PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling…”
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On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans by Gimenez, F., Jiajing Xu, Yi Liu, Liu, T. T., Beaulieu, C. F., Rubin, D. L., Napel, S.

“…We aim to predict radiological observations using computationally-derived imaging features extracted from CT images. Our dataset consists of 79 portal venous…”
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