Zobrazeno 1 - 10
of 790
pro vyhledávání: '"Golland P"'
Autor:
Chen, Yiming, D'Souza, Niharika S., Mandepally, Akshith, Henninger, Patrick, Kashyap, Satyananda, Karani, Neerav, Dey, Neel, Zachary, Marcos, Rizq, Raed, Chouinard, Paul, Golland, Polina, Syeda-Mahmood, Tanveer F.
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical
Externí odkaz:
http://arxiv.org/abs/2408.04826
In this paper, we introduce SpaER, a pioneering method for fetal motion tracking that leverages equivariant filters and self-attention mechanisms to effectively learn spatio-temporal representations. Different from conventional approaches that static
Externí odkaz:
http://arxiv.org/abs/2407.20198
Neural Radiance Fields (NeRFs) have unmatched fidelity on large, real-world scenes. A common approach for scaling NeRFs is to partition the scene into regions, each of which is assigned its own parameters. When implemented naively, such an approach i
Externí odkaz:
http://arxiv.org/abs/2406.11737
Autor:
Bindal, Akanksha, Ramanujam, Sudarshan, Golland, Dave, Hazen, TJ, Jiang, Tina, Zhang, Fengyu, Yan, Peng
In enhancing LinkedIn core content recommendation models, a significant challenge lies in improving their semantic understanding capabilities. This paper addresses the problem by leveraging multi-task learning, a method that has shown promise in vari
Externí odkaz:
http://arxiv.org/abs/2405.11344
The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approa
Externí odkaz:
http://arxiv.org/abs/2404.00132
Autor:
Wang, Peiqi, Shen, Yikang, Guo, Zhen, Stallone, Matthew, Kim, Yoon, Golland, Polina, Panda, Rameswar
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such as the number of tasks. In thi
Externí odkaz:
http://arxiv.org/abs/2402.02318
Autor:
Billot, Benjamin, Dey, Neel, Moyer, Daniel, Hoffmann, Malte, Turk, Esra Abaci, Gagoski, Borjan, Grant, Ellen, Golland, Polina
Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet,
Externí odkaz:
http://arxiv.org/abs/2312.13534
Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g., X-rays) to a high-fidelity 3D preoperative reference scan (e.g., CT). 2D/3D image registration often fails in practice: conventional optimization methods are p
Externí odkaz:
http://arxiv.org/abs/2312.06358
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
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