Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Chen, Yunqiang"'
Publikováno v:
In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 175-184. Springer, Cham, 2019
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows th
Externí odkaz:
http://arxiv.org/abs/2004.01997
Autor:
Reinhold, Jacob C., He, Yufan, Han, Shizhong, Chen, Yunqiang, Gao, Dashan, Lee, Junghoon, Prince, Jerry L., Carass, Aaron
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address
Externí odkaz:
http://arxiv.org/abs/2002.04626
Autor:
Reinhold, Jacob C., He, Yufan, Han, Shizhong, Chen, Yunqiang, Gao, Dashan, Lee, Junghoon, Prince, Jerry L., Carass, Aaron
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative va
Externí odkaz:
http://arxiv.org/abs/2002.04639
Autor:
Xu, Yuchen, Tang, Olivia, Tang, Yucheng, Lee, Ho Hin, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, Landman, Bennett A.
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary gre
Externí odkaz:
http://arxiv.org/abs/2002.04098
Autor:
Xu, Yuchen, Tang, Olivia, Tang, Yucheng, Lee, Ho Hin, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, Landman, Bennett A.
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation
Externí odkaz:
http://arxiv.org/abs/2002.04102
Autor:
Huo, Yuankai, Tang, Yucheng, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Bao, Shunxing, De, Smita, Terry, James G., Carr, Jeffrey J., Abramson, Richard G., Landman, Bennett A.
Publikováno v:
Journal of Medical Imaging 6.4 (2019): 044005
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT). However, the effectiveness of tissue window normalization is quest
Externí odkaz:
http://arxiv.org/abs/1912.00420
Autor:
Tang, Yucheng, Lee, Ho Hin, Xu, Yuchen, Tang, Olivia, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Bermudez, Camilo, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, Landman, Bennett A.
Publikováno v:
SPIE2020
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contr
Externí odkaz:
http://arxiv.org/abs/1911.06395
Autor:
Lee, Ho Hin, Tang, Yucheng, Tang, Olivia, Xu, Yuchen, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, Landman, Bennett A.
Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabel
Externí odkaz:
http://arxiv.org/abs/1911.05113
Akademický článek
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Autor:
Zhong, Lifan, Wang, Qianru, Kou, Zhixiong, Gan, Lianfang, Yang, Zhaoxin, Pan, Junhua, Huang, Ling, Chen, Yunqiang
Publikováno v:
Journal of Cellular & Molecular Medicine; Nov2024, Vol. 28 Issue 21, p1-16, 16p
