Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Taie, Mais Al"'
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
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
Autor:
Woodland, McKell, Castelo, Austin, Taie, Mais Al, Silva, Jessica Albuquerque Marques, Eltaher, Mohamed, Mohn, Frank, Shieh, Alexander, Kundu, Suprateek, Yung, Joshua P., Patel, Ankit B., Brock, Kristy K.
Publikováno v:
MICCAI 2024. Lecture Notes in Computer Science, vol 15012. Springer, Cham (2024)
Fr\'echet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is to adapt FID to medical imaging
Externí odkaz:
http://arxiv.org/abs/2311.13717
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