Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Netherton, Tucker J"'
Autor:
Cheung, Matt Y., Netherton, Tucker J., Court, Laurence E., Veeraraghavan, Ashok, Balakrishnan, Guha
Uncertainty quantification is crucial to account for the imperfect predictions of machine learning algorithms for high-impact applications. Conformal prediction (CP) is a powerful framework for uncertainty quantification that generates calibrated pre
Externí odkaz:
http://arxiv.org/abs/2410.05263
Autor:
Woodland, McKell, Patel, Nihil, Castelo, Austin, Taie, Mais Al, Eltaher, Mohamed, Yung, Joshua P., Netherton, Tucker J., Calderone, Tiffany L., Sanchez, Jessica I., Cleere, Darrel W., Elsaiey, Ahmed, Gupta, Nakul, Victor, David, Beretta, Laura, Patel, Ankit B., Brock, Kristy K.
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024) 2006
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate auto
Externí odkaz:
http://arxiv.org/abs/2408.02761
Autor:
Cheung, Matt Y, Netherton, Tucker J, Court, Laurence E, Veeraraghavan, Ashok, Balakrishnan, Guha
Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an i
Externí odkaz:
http://arxiv.org/abs/2404.15274
Autor:
Wahid, Kareem A., Cardenas, Carlos E., Marquez, Barbara, Netherton, Tucker J., Kann, Benjamin H., Court, Laurence E., He, Renjie, Naser, Mohamed A., Moreno, Amy C., Fuller, Clifton D., Fuentes, David
Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-co
Externí odkaz:
http://arxiv.org/abs/2310.10867
Autor:
Woodland, McKell, Patel, Nihil, Taie, Mais Al, Yung, Joshua P., Netherton, Tucker J., Patel, Ankit B., Brock, Kristy K.
Publikováno v:
In: UNSURE 2023. LNCS, vol 14291. Springer, Cham (2023)
Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out-of-distribution (OOD) images at inference to protect against automati
Externí odkaz:
http://arxiv.org/abs/2308.03723
Autor:
Wahid, Kareem A., Kaffey, Zaphanlene Y., Farris, David P., Humbert-Vidan, Laia, Moreno, Amy C., Rasmussen, Mathis, Ren, Jintao, Naser, Mohamed A., Netherton, Tucker J., Korreman, Stine, Balakrishnan, Guha, Fuller, Clifton D., Fuentes, David, Dohopolski, Michael J.
Publikováno v:
In Radiotherapy and Oncology December 2024 201
Autor:
Maroongroge, Sean, Mohamed, Abdallah SR., Nguyen, Callistus, Guma De la Vega, Jean, Frank, Steven J., Garden, Adam S., Gunn, Brandon G., Lee, Anna, Mayo, Lauren, Moreno, Amy, Morrison, William H., Phan, Jack, Spiotto, Michael T., Court, Laurence E., Fuller, Clifton D., Rosenthal, David I., Netherton, Tucker J.
Publikováno v:
In Physics and Imaging in Radiation Oncology January 2024 29
Autor:
Sekuboyina, Anjany, Husseini, Malek E., Bayat, Amirhossein, Löffler, Maximilian, Liebl, Hans, Li, Hongwei, Tetteh, Giles, Kukačka, Jan, Payer, Christian, Štern, Darko, Urschler, Martin, Chen, Maodong, Cheng, Dalong, Lessmann, Nikolas, Hu, Yujin, Wang, Tianfu, Yang, Dong, Xu, Daguang, Ambellan, Felix, Amiranashvili, Tamaz, Ehlke, Moritz, Lamecker, Hans, Lehnert, Sebastian, Lirio, Marilia, de Olaguer, Nicolás Pérez, Ramm, Heiko, Sahu, Manish, Tack, Alexander, Zachow, Stefan, Jiang, Tao, Ma, Xinjun, Angerman, Christoph, Wang, Xin, Brown, Kevin, Kirszenberg, Alexandre, Puybareau, Élodie, Chen, Di, Bai, Yiwei, Rapazzo, Brandon H., Yeah, Timyoas, Zhang, Amber, Xu, Shangliang, Hou, Feng, He, Zhiqiang, Zeng, Chan, Xiangshang, Zheng, Liming, Xu, Netherton, Tucker J., Mumme, Raymond P., Court, Laurence E., Huang, Zixun, He, Chenhang, Wang, Li-Wen, Ling, Sai Ho, Huynh, Lê Duy, Boutry, Nicolas, Jakubicek, Roman, Chmelik, Jiri, Mulay, Supriti, Sivaprakasam, Mohanasankar, Paetzold, Johannes C., Shit, Suprosanna, Ezhov, Ivan, Wiestler, Benedikt, Glocker, Ben, Valentinitsch, Alexander, Rempfler, Markus, Menze, Björn H., Kirschke, Jan S.
Publikováno v:
Medical Image Analysis, Volume 73, October 2021, 102166
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and p
Externí odkaz:
http://arxiv.org/abs/2001.09193
Autor:
Oh, Kyuhak, Gronberg, Mary P., Netherton, Tucker J., Sengupta, Bishwambhar, Cardenas, Carlos E., Court, Laurence E., Ford, Eric C.
Publikováno v:
In Physics and Imaging in Radiation Oncology April 2023 26
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