Zobrazeno 1 - 10
of 1 301
pro vyhledávání: '"GEE, JAMES"'
Autor:
Yao, Michael S., Chae, Allison, Kahn Jr., Charles E., Witschey, Walter R., Gee, James C., Sagreiya, Hersh, Bastani, Osbert
Diagnostic imaging studies are an increasingly important component of the workup and management of acutely presenting patients. However, ordering appropriate imaging studies according to evidence-based medical guidelines is a challenging task with a
Externí odkaz:
http://arxiv.org/abs/2409.19177
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak p
Externí odkaz:
http://arxiv.org/abs/2408.05839
We study the intriguing connection between visual data, deep networks, and the brain. Our method creates a universal channel alignment by using brain voxel fMRI response prediction as the training objective. We discover that deep networks, trained wi
Externí odkaz:
http://arxiv.org/abs/2406.18344
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, DLIR methods forego many of the benefits of cla
Externí odkaz:
http://arxiv.org/abs/2406.07361
Autor:
Yang, Yue, Gandhi, Mona, Wang, Yufei, Wu, Yifan, Yao, Michael S., Callison-Burch, Chris, Gee, James C., Yatskar, Mark
While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled
Externí odkaz:
http://arxiv.org/abs/2405.14839
Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging. Leveraging Neural Ordinary Differential Equations (ODE) for registrat
Externí odkaz:
http://arxiv.org/abs/2404.02106
Diffeomorphic Image Registration is a critical part of the analysis in various imaging modalities and downstream tasks like image translation, segmentation, and atlas building. Registration algorithms based on optimization have stood the test of time
Externí odkaz:
http://arxiv.org/abs/2404.01249
Autor:
Wu, Yifan, Liu, Yang, Yang, Yue, Yao, Michael S., Yang, Wenli, Shi, Xuehui, Yang, Lihong, Li, Dongjun, Liu, Yueming, Gee, James C., Yang, Xuan, Wei, Wenbin, Gu, Shi
Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification. The advent of machine learning offers a promising solution, while the development of such technologies
Externí odkaz:
http://arxiv.org/abs/2403.05606
Autor:
Yao, Michael S., Zeng, Yimeng, Bastani, Hamsa, Gardner, Jacob, Gee, James C., Bastani, Osbert
Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonly encountered in protein design, robotics, and clinical medicine where
Externí odkaz:
http://arxiv.org/abs/2402.06532
Coronary angiography is the gold standard imaging technique for studying and diagnosing coronary artery disease. However, the resulting 2D X-ray projections lose 3D information and exhibit visual ambiguities. In this work, we aim to establish dense c
Externí odkaz:
http://arxiv.org/abs/2312.11593