Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Xu, Zhoubing"'
Fluorescence labeling is the standard approach to reveal cellular structures and other subcellular constituents for microscopy images. However, this invasive procedure may perturb or even kill the cells and the procedure itself is highly time-consumi
Externí odkaz:
http://arxiv.org/abs/2406.15716
Unsupervised cross-modality domain adaptation is a challenging task in medical image analysis, and it becomes more challenging when source and target domain data are collected from multiple institutions. In this paper, we present our solution to tack
Externí odkaz:
http://arxiv.org/abs/2311.12437
Autor:
Liu, Han, Li, Hao, Yao, Xing, Fan, Yubo, Hu, Dewei, Dawant, Benoit, Nath, Vishwesh, Xu, Zhoubing, Oguz, Ipek
Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bot
Externí odkaz:
http://arxiv.org/abs/2307.12004
Autor:
Liu, Han, Xu, Zhoubing, Gao, Riqiang, Li, Hao, Wang, Jianing, Chabin, Guillaume, Oguz, Ipek, Grbic, Sasa
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially la
Externí odkaz:
http://arxiv.org/abs/2304.14030
Autor:
Yu, Xin, Yang, Qi, Zhou, Yinchi, Cai, Leon Y., Gao, Riqiang, Lee, Ho Hin, Li, Thomas, Bao, Shunxing, Xu, Zhoubing, Lasko, Thomas A., Abramson, Richard G., Zhang, Zizhao, Huo, Yuankai, Landman, Bennett A., Tang, Yucheng
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches
Externí odkaz:
http://arxiv.org/abs/2209.14378
Autor:
Gao, Riqiang, Li, Thomas, Tang, Yucheng, Xu, Zhoubing, Kammer, Michael, Antic, Sanja L., Sandler, Kim, Moldonado, Fabien, Lasko, Thomas A., Landman, Bennett
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare. Moreover, the long-tail distribution poses great ch
Externí odkaz:
http://arxiv.org/abs/2206.08833
Autor:
Yu, Xin, Tang, Yucheng, Zhou, Yinchi, Gao, Riqiang, Yang, Qi, Lee, Ho Hin, Li, Thomas, Bao, Shunxing, Huo, Yuankai, Xu, Zhoubing, Lasko, Thomas A., Abramson, Richard G., Landman, Bennett A.
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology. However, the development and evaluation of the transformer model to segment the renal cortex, medulla, and collect
Externí odkaz:
http://arxiv.org/abs/2203.02430
Autor:
Liu, Siqi, Georgescu, Bogdan, Xu, Zhoubing, Yoo, Youngjin, Chabin, Guillaume, Chaganti, Shikha, Grbic, Sasa, Piat, Sebastian, Teixeira, Brian, Balachandran, Abishek, RS, Vishwanath, Re, Thomas, Comaniciu, Dorin
The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Several studies have shown that tomographic patterns seen on chest Computed Tomography (CT), such as ground-glass opacit
Externí odkaz:
http://arxiv.org/abs/2005.01903
Autor:
Chaganti, Shikha, Balachandran, Abishek, Chabin, Guillaume, Cohen, Stuart, Flohr, Thomas, Georgescu, Bogdan, Grenier, Philippe, Grbic, Sasa, Liu, Siqi, Mellot, François, Murray, Nicolas, Nicolaou, Savvas, Parker, William, Re, Thomas, Sanelli, Pina, Sauter, Alexander W., Xu, Zhoubing, Yoo, Youngjin, Ziebandt, Valentin, Comaniciu, Dorin
Publikováno v:
Radiology: Artificial Intelligence, Vol. 2, No. 4, 2020
Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. Materials and Methods: In this retrospective study
Externí odkaz:
http://arxiv.org/abs/2004.01279
Autor:
Zhang, Donghao, Liu, Siqi, Chaganti, Shikha, Gibson, Eli, Xu, Zhoubing, Grbic, Sasa, Cai, Weidong, Comaniciu, Dorin
With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images ca
Externí odkaz:
http://arxiv.org/abs/2003.07999