Zobrazeno 1 - 10
of 1 092
pro vyhledávání: '"Young Sean"'
Autor:
Young, Sean I.
In recent years, compression of large language models (LLMs) has emerged as an important problem to enable language model deployment on resource-constrained devices, reduce computational costs, and mitigate the environmental footprint of large-scale
Externí odkaz:
http://arxiv.org/abs/2409.02026
Autor:
Abulnaga, S. Mazdak, Dey, Neel, Young, Sean I., Pan, Eileen, Hobgood, Katherine I., Wang, Clinton J., Grant, P. Ellen, Turk, Esra Abaci, Golland, Polina
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and
Externí odkaz:
http://arxiv.org/abs/2312.05148
Autor:
Laso, Pablo, Cerri, Stefano, Sorby-Adams, Annabel, Guo, Jennifer, Mateen, Farrah, Goebl, Philipp, Wu, Jiaming, Liu, Peirong, Li, Hongwei, Young, Sean I., Billot, Benjamin, Puonti, Oula, Sze, Gordon, Payabavash, Sam, DeHavenon, Adam, Sheth, Kevin N., Rosen, Matthew S., Kirsch, John, Strisciuglio, Nicola, Wolterink, Jelmer M., Eshaghi, Arman, Barkhof, Frederik, Kimberly, W. Taylor, Iglesias, Juan Eugenio
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods requir
Externí odkaz:
http://arxiv.org/abs/2312.05119
In magnetic resonance imaging (MRI), slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising, current SVR methods require mul
Externí odkaz:
http://arxiv.org/abs/2312.03102
Autor:
Wang, Alan Q., Karaman, Batuhan K., Kim, Heejong, Rosenthal, Jacob, Saluja, Rachit, Young, Sean I., Sabuncu, Mert R.
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What go
Externí odkaz:
http://arxiv.org/abs/2310.01685
Autor:
French, Matthew G., Talou, Gonzalo D. Maso, Gamage, Thiranja P. Babarenda, Nash, Martyn P., Nielsen, Poul M., Doyle, Anthony J., Iglesias, Juan Eugenio, Balbastre, Yaël, Young, Sean I.
In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment. While learning-based registration methods have recently become the state
Externí odkaz:
http://arxiv.org/abs/2309.13777
Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters. While one can solve such problems by train
Externí odkaz:
http://arxiv.org/abs/2303.09642
Autor:
Abulnaga, S. Mazdak, Young, Sean I., Hobgood, Katherine, Pan, Eileen, Wang, Clinton J., Grant, P. Ellen, Turk, Esra Abaci, Golland, Polina
Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Measuring signal changes over time requires segmenting the placenta in ea
Externí odkaz:
http://arxiv.org/abs/2208.02895
Autor:
Young, Sean I., Balbastre, Yaël, Dalca, Adrian V., Wells, William M., Iglesias, Juan Eugenio, Fischl, Bruce
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks. These approaches utilize a loss
Externí odkaz:
http://arxiv.org/abs/2205.07399