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pro vyhledávání: '"Kim, Boah"'
Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are concomitant inaccuracies that exist within the DICOM header fields of these
Externí odkaz:
http://arxiv.org/abs/2405.08247
Autor:
Zhuang, Yan, Mathai, Tejas Sudharshan, Mukherjee, Pritam, Khoury, Brandon, Kim, Boah, Hou, Benjamin, Rabbee, Nusrat, Suri, Abhinav, Summers, Ronald M.
Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have focused on delineating a limited set of abdominal structures (13 types).
Externí odkaz:
http://arxiv.org/abs/2405.05944
Autor:
Helm, Kimberly, Mathai, Tejas Sudharshan, Kim, Boah, Mukherjee, Pritam, Liu, Jianfei, Summers, Ronald M.
Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exist due to wide variations in im
Externí odkaz:
http://arxiv.org/abs/2402.08098
Autor:
Zhuang, Yan, Hou, Benjamin, Mathai, Tejas Sudharshan, Mukherjee, Pritam, Kim, Boah, Summers, Ronald M.
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal
Externí odkaz:
http://arxiv.org/abs/2312.06453
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual
Externí odkaz:
http://arxiv.org/abs/2308.00193
Autor:
Kim, Boah1,2 (AUTHOR) boah_kim@sfu.ca, Wister, Andrew1,2 (AUTHOR), Mitchell, Barbara1,3 (AUTHOR), Li, Lun2,4 (AUTHOR), Kadowaki, Laura1,2 (AUTHOR)
Publikováno v:
BMC Health Services Research. 10/1/2024, Vol. 24 Issue 1, p1-14. 14p.
Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are r
Externí odkaz:
http://arxiv.org/abs/2209.14566
Autor:
Kim, Boah, Ye, Jong Chul
Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively stu
Externí odkaz:
http://arxiv.org/abs/2206.13295
Deformable image registration is one of the fundamental tasks in medical imaging. Classical registration algorithms usually require a high computational cost for iterative optimizations. Although deep-learning-based methods have been developed for fa
Externí odkaz:
http://arxiv.org/abs/2112.05149
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentralized data while maintaining data privacy. For example, this enables
Externí odkaz:
http://arxiv.org/abs/2111.01338