Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Millis, Bryan A."'
Autor:
Leng, Haoju, Deng, Ruining, Bao, Shunxing, Fang, Dazheng, Millis, Bryan A., Tang, Yucheng, Yang, Haichun, Wang, Xiao, Peng, Yifan, Wan, Lipeng, Huo, Yuankai
When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the comp
Externí odkaz:
http://arxiv.org/abs/2308.05784
Autor:
Zhao, Mengyang, Liu, Quan, Jha, Aadarsh, Deng, Ruining, Yao, Tianyuan, Mahadevan-Jansen, Anita, Tyska, Matthew J., Millis, Bryan A., Huo, Yuankai
Recent advances in bioimaging have provided scientists a superior high spatial-temporal resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3D biomedical video analysis is lagging, impeded by resource insensitiv
Externí odkaz:
http://arxiv.org/abs/2106.11480
Autor:
Newman, Malachy, Rasiah, Pratheepa Kumari, Kusunose, Jiro, Rex, Tonia S., Mahadevan-Jansen, Anita, Hardenburger, Jacob, Jansen, E. Duco, Millis, Bryan, Caskey, Charles F.
Publikováno v:
In Ultrasound in Medicine & Biology March 2024 50(3):341-351
Autor:
Liu, Quan, Gaeta, Isabella M., Zhao, Mengyang, Deng, Ruining, Jha, Aadarsh, Millis, Bryan A., Mahadevan-Jansen, Anita, Tyska, Matthew J., Huo, Yuankai
Background: The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of two stages: (1) performing instance object segmentation of each frame
Externí odkaz:
http://arxiv.org/abs/2101.00567
Autor:
Liu, Quan, Gaeta, Isabella M., Zhao, Mengyang, Deng, Ruining, Jha, Aadarsh, Millis, Bryan A., Mahadevan-Jansen, Anita, Tyska, Matthew J., Huo, Yuankai
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos. The recent single-stage pixel-embedding based deep learning approach has shown its superior performance compared with "segment-then-as
Externí odkaz:
http://arxiv.org/abs/2011.01009
Autor:
Deng, Ruining, Liu, Quan, Bao, Shunxing, Jha, Aadarsh, Chang, Catie, Millis, Bryan A., Tyska, Matthew J., Huo, Yuankai
Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biomedical images naturally contain image-wise labels in many scenarios. Th
Externí odkaz:
http://arxiv.org/abs/2011.00794
The unsupervised segmentation is an increasingly popular topic in biomedical image analysis. The basic idea is to approach the supervised segmentation task as an unsupervised synthesis problem, where the intensity images can be transferred to the ann
Externí odkaz:
http://arxiv.org/abs/2010.11438
Autor:
Zhao, Mengyang, Jha, Aadarsh, Liu, Quan, Millis, Bryan A., Mahadevan-Jansen, Anita, Lu, Le, Landman, Bennett A., Tyskac, Matthew J., Huo, Yuankai
Recently, single-stage embedding based deep learning algorithms gain increasing attention in cell segmentation and tracking. Compared with the traditional "segment-then-associate" two-stage approach, a single-stage algorithm not only simultaneously a
Externí odkaz:
http://arxiv.org/abs/2007.14283
Autor:
Cencer, Caroline S., Silverman, Jennifer B., Meenderink, Leslie M., Krystofiak, Evan S., Millis, Bryan A., Tyska, Matthew J.
Publikováno v:
In Developmental Cell 23 October 2023 58(20):2048-2062
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