Ngā hua rapu - Ha, Richard

Understanding artificial intelligence based radiology studies: What is overfitting? mā Mutasa, Simukayi, Sun, Shawn, Ha, Richard

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Commentary: Superior vena cava syndrome should not hinder use of a percutaneous right ventricular assist device mā Ha, Richard V., Nguyen, Tom C.

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Optimal timing of same-admission orthotopic heart transplantation after left ventricular assist device implantation mā Gulati, Gunsagar, Ouyang, David, Ha, Richard, Banerjee, Dipanjan

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Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment mā Mango, Victoria L., Sun, Mary, Wynn, Ralph T., Ha, Richard

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Ultrasonographic features and clinical implications of benign palpable breast lesions in young women mā Ha, Richard, Kim, Hyonah, Mango, Victoria, Wynn, Ralph, Comstock, Christopher

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Innovations in image-guided preoperative breast lesion localization mā Cheang, Ellen, Ha, Richard, Thornton, Cynthia M, Mango, Victoria L

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Biventricular VAD versus LVAD for right heart failure mā Boulate, David, Marques, Michael A., Ha, Richard, Banerjee, Dipanjan, Haddad, Francois

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Breast MRI Screening for Average-Risk Women: A Monte Carlo Simulation Cost–Benefit Analysis mā Mango, Victoria L., Goel, Akshay, Mema, Eralda, Kwak, Ellie, Ha, Richard

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Simultaneous ramp right heart catheterization and echocardiography in a ReliantHeart left ventricular assist device mā Banerjee, Dipanjan, Dutt, Debleena, Duclos, Sebastien, Sallam, Karim, Wheeler, Matthew, Ha, Richard

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Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment mā Manley, Haley, Mutasa, Simukayi, Chang, Peter, Desperito, Elise, Crew, Katherine, Ha, Richard

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A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database mā Liu, Michael Z., Mutasa, Simukayi, Chang, Peter, Siddique, Maham, Jambawalikar, Sachin, Ha, Richard

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Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study mā Ha, Richard, Mema, Eralda, Guo, Xiaotao, Mango, Victoria, Desperito, Elise, Ha, Jason, Wynn, Ralph, Zhao, Binsheng

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A Single-Institution Experience in the Preoperative Selection of DCIS Patients for IORT using the ASTRO Consensus Guidelines mā Chin, Christine, Hirji, Sitara, Onishi, Maika, Ha, Richard, Taback, Bret, Horowitz, David P., Connolly, Eileen P.

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Stereotactic breast biopsy efficiency: Does a pre-biopsy grid image help? mā Mango, Victoria L., D’Alessio, Donna, Morris, Elizabeth A., Ha, Richard, Bernard-Davila, Blanca, Jochelson, Maxine S.

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Influence of Durable Mechanical Circulatory Support and Allosensitization on Mortality After Heart Transplantation mā Chiu, Peter, Schaffer, Justin M., Oyer, Philip E., Pham, Michael, Banerjee, Dipanjan, Woo, Y. Joseph, Ha, Richard

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Evaluation of Partial Breast Reirradiation with Intraoperative Radiotherapy after Prior Thoracic Radiation: A Single-Institution Report of Outcomes and Toxicity mā Chin, Christine, Jadeja, Priya, Taback, Bret, Horowitz, David P., Feldman, Sheldon M., Ha, Richard, Connolly, Eileen P.

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Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset mā Ha, Richard, Chang, Peter, Karcich, Jenika, Mutasa, Simukayi, Fardanesh, Reza, Wynn, Ralph T., Liu, Michael Z., Jambawalikar, Sachin

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Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement mā Ha, Richard, Chang, Peter, Mema, Eralda, Mutasa, Simukayi, Karcich, Jenika, Wynn, Ralph T., Liu, Michael Z., Jambawalikar, Sachin

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Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network–Based Machine Learning Approach Using Mammographic Image Data... mā Ha, Richard, Mutasa, Simukayi, Sant, Eduardo Pascual Van, Karcich, Jenika, Chin, Christine, Liu, Michael Z., Jambawalikar, Sachin

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Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset mā Ha, Richard, Chang, Peter, Karcich, Jenika, Mutasa, Simukayi, Pascual Van Sant, Eduardo, Liu, Michael Z., Jambawalikar, Sachin

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