Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks von Shin, Hoo-Chang, Tenenholtz, Neil A., Rogers, Jameson K., Schwarz, Christopher G., Senjem, Matthew L., Gunter, Jeffrey L., Andriole, Katherine P., Michalski, Mark

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Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets von Gauriau, Romane, Bridge, Christopher, Chen, Lina, Kitamura, Felipe, Tenenholtz, Neil A., Kirsch, John E., Andriole, Katherine P., Michalski, Mark H., Bizzo, Bernardo C.

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The Need for a Machine Learning Curriculum for Radiologists von Wood, Monica J., Tenenholtz, Neil A., Geis, J. Raymond, Michalski, Mark H., Andriole, Katherine P.

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Automated De-Identification: Embracing the Imperfect von Tenenholtz, Neil A, Wood, Monica J

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On the design of an interactive, patient-specific surgical simulator for mitral valve repair von Tenenholtz, Neil A., Hammer, Peter E., Schneider, Robert J., Vasilyev, Nikolay V., Howe, Robert D.

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Patient-Specific Mitral Leaflet Segmentation from 4D Ultrasound von Schneider, Robert J., Tenenholtz, Neil A., Perrin, Douglas P., Marx, Gerald R., del Nido, Pedro J., Howe, Robert D.

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Fast Simulation of Mitral Annuloplasty for Surgical Planning von Tenenholtz, Neil A., Hammer, Peter E., Fabozzo, Assunta, Feins, Eric N., del Nido, Pedro J., Howe, Robert D.

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Abstract WP78: Deep Learning for Acute Ischemic Stroke on Diffusion MRI: Performance Analysis in a Consecutive Cohort von Bizzo, Bernardo, Bridge, Christopher, Gauriau, Romane, Wiggins, Walter, Caton, Michael T, Hillis, James, Wright, Bradley, Tenenholtz, Neil A, Andriole, Katherine P, Michalski, Mark, Gonzalez, Gilberto

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DeepAAA: Clinically Applicable and Generalizable Detection of Abdominal Aortic Aneurysm Using Deep Learning von Lu, Jen-Tang, Brooks, Rupert, Hahn, Stefan, Chen, Jin, Buch, Varun, Kotecha, Gopal, Andriole, Katherine P., Ghoshhajra, Brian, Pinto, Joel, Vozila, Paul, Michalski, Mark, Tenenholtz, Neil A.

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DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning von Lu, Jen-Tang, Brooks, Rupert, Hahn, Stefan, Chen, Jin, Buch, Varun, Kotecha, Gopal, Andriole, Katherine P, Ghoshhajra, Brian, Pinto, Joel, Vozila, Paul, Michalski, Mark, Tenenholtz, Neil A

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Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks von Shin, Hoo-Chang, Tenenholtz, Neil A, Rogers, Jameson K, Schwarz, Christopher G, Senjem, Matthew L, Gunter, Jeffrey L, Andriole, Katherine, Michalski, Mark

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DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning von Jen-Tang, Lu, Brooks, Rupert, Hahn, Stefan, Chen, Jin, Buch, Varun, Kotecha, Gopal, Andriole, Katherine P, Ghoshhajra, Brian, Pinto, Joel, Vozila, Paul, Michalski, Mark, Tenenholtz, Neil A

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Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks von Hoo-Chang, Shin, Tenenholtz, Neil A, Rogers, Jameson K, Schwarz, Christopher G, Senjem, Matthew L, Gunter, Jeffrey L, Andriole, Katherine, Michalski, Mark

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A foundation model for clinical-grade computational pathology and rare cancers detection von Vorontsov, Eugene, Bozkurt, Alican, Casson, Adam, Shaikovski, George, Zelechowski, Michal, Severson, Kristen, Zimmermann, Eric, Hall, James, Tenenholtz, Neil, Fusi, Nicolo, Yang, Ellen, Mathieu, Philippe, van Eck, Alexander, Lee, Donghun, Viret, Julian, Robert, Eric, Wang, Yi Kan, Kunz, Jeremy D., Lee, Matthew C. H., Bernhard, Jan H., Godrich, Ran A., Oakley, Gerard, Millar, Ewan, Hanna, Matthew, Wen, Hannah, Retamero, Juan A., Moye, William A., Yousfi, Razik, Kanan, Christopher, Klimstra, David S., Rothrock, Brandon, Liu, Siqi, Fuchs, Thomas J.

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Abstract WP68: Interpretable Deep Learning-based Characterization of Intracranial Hemorrhage on Head CT von Bizzo, Bernardo, Hashemian, Behrooz, McNitt, Troy, Caton, Michael T, Wiggins, Walter, Hillis, James, Tenenholtz, Neil, Kitamura, Felipe, Gonzalez, Gilberto, Michalski, Mark, Andriole, Katherine, Pomerantz, Stuart R

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4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences von Myronenko, Andriy, Yang, Dong, Buch, Varun, Xu, Daguang, Ihsani, Alvin, Doyle, Sean, Michalski, Mark, Tenenholtz, Neil, Roth, Holger

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RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models von Maleki, Farhad, Moy, Linda, Forghani, Reza, Ghosh, Tapotosh, Ovens, Katie, Langer, Steve, Rouzrokh, Pouria, Khosravi, Bardia, Ganjizadeh, Ali, Warren, Daniel, Daneshjou, Roxana, Moassefi, Mana, Avval, Atlas Haddadi, Sotardi, Susan, Tenenholtz, Neil, Kitamura, Felipe, Kline, Timothy

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Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks von Bridge, Christopher P., Rosenthal, Michael, Wright, Bradley, Kotecha, Gopal, Fintelmann, Florian, Troschel, Fabian, Miskin, Nityanand, Desai, Khanant, Wrobel, William, Babic, Ana, Khalaf, Natalia, Brais, Lauren, Welch, Marisa, Zellers, Caitlin, Tenenholtz, Neil, Michalski, Mark, Wolpin, Brian, Andriole, Katherine

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Improving mitosis detection on histopathology images using large vision-language models von Ding, Ruiwen, Hall, James, Tenenholtz, Neil, Severson, Kristen

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FACILITATING ARTIFICIAL INTELLIGENCE INTEGRATION INTO SYSTEMS USING A DISTRIBUTED LEARNING PLATFORM von DOYLE SEAN, KALAFUT JOHN, POMERANTZ STUART, MICHALSKI MARK, DREYER KEITH, TENENHOLTZ NEIL

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