Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Chen, Antong"'
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
Autor:
Pan, Shaoyan, Liu, Yiqiao, Halek, Sarah, Tomaszewski, Michal, Wang, Shubing, Baumgartner, Richard, Yuan, Jianda, Goldmacher, Gregory, Chen, Antong
In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics. However, following the RECIST criteria, radiologists routinely only delineate each lesion on the axial slice showing the
Externí odkaz:
http://arxiv.org/abs/2309.01823
Autor:
Soans, Rajath, Gleason, Alexa, Shah, Tosha, Miller, Corey, Robinson, Barbara, Brannen, Kimberly, Chen, Antong
In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment of drug-induced skeletal abnormalities as a required study in developme
Externí odkaz:
http://arxiv.org/abs/2307.06392
Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augm
Externí odkaz:
http://arxiv.org/abs/2005.01683
Autor:
Chen, Antong, Saouaf, Jennifer, Zhou, Bo, Crawford, Randolph, Yuan, Jianda, Ma, Junshui, Baumgartner, Richard, Wang, Shubing, Goldmacher, Gregory
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach starts with the classification of l
Externí odkaz:
http://arxiv.org/abs/2003.02943
It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes importa
Externí odkaz:
http://arxiv.org/abs/1910.06428
Publikováno v:
In Powder Technology 1 August 2023 426
Autor:
Zhang, Dongqing, Icke, Ilknur, Dogdas, Belma, Parimal, Sarayu, Sampath, Smita, Forbes, Joseph, Bagchi, Ansuman, Chin, Chih-Liang, Chen, Antong
Automatic segmentation of left ventricle (LV) myocardium in cardiac short-axis cine MR images acquired on subjects with myocardial infarction is a challenging task, mainly because of the various types of image inhomogeneity caused by the infarctions.
Externí odkaz:
http://arxiv.org/abs/1811.06051
Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large
Externí odkaz:
http://arxiv.org/abs/1811.04437
Autor:
Chen, Antong
Cancers in the head and neck region account for approximately 3 percent of all cancers in the United States, as it is reported by the American Cancer Society. Depending on the location and stage of the cancers, surgery, chemotherapy and radiation the
Autor:
Ma, Xiangyu, Kittikunakorn, Nada, Sorman, Bradley, Xi, Hanmi, Chen, Antong, Marsh, Mike, Mongeau, Arthur, Piché, Nicolas, Williams, Robert O., III, Skomski, Daniel
Publikováno v:
In Journal of Pharmaceutical Sciences April 2020 109(4):1547-1557