Zobrazeno 1 - 10
of 2 026
pro vyhledávání: '"Netherton A"'
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:
Sun, Yiran, Baroudi, Hana, Netherton, Tucker, Court, Laurence, Mawlawi, Osama, Veeraraghavan, Ashok, Balakrishnan, Guha
Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam radiotherapy. Unfortunately, the availability of CT scanners in low- and
Externí odkaz:
http://arxiv.org/abs/2408.15118
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:
Celaya, Adrian, Lim, Evan, Glenn, Rachel, Mi, Brayden, Balsells, Alex, Schellingerhout, Dawid, Netherton, Tucker, Chung, Caroline, Riviere, Beatrice, Fuentes, David
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new methods mak
Externí odkaz:
http://arxiv.org/abs/2407.21343
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
CT scans are the standard-of-care for many clinical ailments, and are needed for treatments like external beam radiotherapy. Unfortunately, CT scanners are rare in low and mid-resource settings due to their costs. Planar X-ray radiography units, in c
Externí odkaz:
http://arxiv.org/abs/2308.02100
Frequency-modulated (FM) laser combs, which offer a periodic quasi-continuous-wave output and a flat-topped optical spectrum, are emerging as a promising solution for wavelength-division multiplexing applications, precision metrology, and ultrafast o
Externí odkaz:
http://arxiv.org/abs/2306.15125
Autor:
Nihil Patel, Adrian Celaya, Mohamed Eltaher, Rachel Glenn, Kari Brewer Savannah, Kristy K. Brock, Jessica I. Sanchez, Tiffany L. Calderone, Darrel Cleere, Ahmed Elsaiey, Matthew Cagley, Nakul Gupta, David Victor, Laura Beretta, Eugene J. Koay, Tucker J. Netherton, David T. Fuentes
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the
Externí odkaz:
https://doaj.org/article/351f87338fd84c5aad535e50e0854a47
