Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Chen, Po-Hsuan Cameron"'
Autor:
Lai, Jeremy, Ahmed, Faruk, Vijay, Supriya, Jaroensri, Tiam, Loo, Jessica, Vyawahare, Saurabh, Agarwal, Saloni, Jamil, Fayaz, Matias, Yossi, Corrado, Greg S., Webster, Dale R., Krause, Jonathan, Liu, Yun, Chen, Po-Hsuan Cameron, Wulczyn, Ellery, Steiner, David F.
Task-specific deep learning models in histopathology offer promising opportunities for improving diagnosis, clinical research, and precision medicine. However, development of such models is often limited by availability of high-quality data. Foundati
Externí odkaz:
http://arxiv.org/abs/2310.13259
Autor:
Azizi, Shekoofeh, Culp, Laura, Freyberg, Jan, Mustafa, Basil, Baur, Sebastien, Kornblith, Simon, Chen, Ting, MacWilliams, Patricia, Mahdavi, S. Sara, Wulczyn, Ellery, Babenko, Boris, Wilson, Megan, Loh, Aaron, Chen, Po-Hsuan Cameron, Liu, Yuan, Bavishi, Pinal, McKinney, Scott Mayer, Winkens, Jim, Roy, Abhijit Guha, Beaver, Zach, Ryan, Fiona, Krogue, Justin, Etemadi, Mozziyar, Telang, Umesh, Liu, Yun, Peng, Lily, Corrado, Greg S., Webster, Dale R., Fleet, David, Hinton, Geoffrey, Houlsby, Neil, Karthikesalingam, Alan, Norouzi, Mohammad, Natarajan, Vivek
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical setting
Externí odkaz:
http://arxiv.org/abs/2205.09723
Autor:
Kazemzadeh, Sahar, Yu, Jin, Jamshy, Shahar, Pilgrim, Rory, Nabulsi, Zaid, Chen, Christina, Beladia, Neeral, Lau, Charles, McKinney, Scott Mayer, Hughes, Thad, Kiraly, Atilla, Kalidindi, Sreenivasa Raju, Muyoyeta, Monde, Malemela, Jameson, Shih, Ting, Corrado, Greg S., Peng, Lily, Chou, Katherine, Chen, Po-Hsuan Cameron, Liu, Yun, Eswaran, Krish, Tse, Daniel, Shetty, Shravya, Prabhakara, Shruthi
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a barrier. We trained a deep learning system (DLS) to detect active pulmona
Externí odkaz:
http://arxiv.org/abs/2105.07540
Autor:
Wulczyn, Ellery, Nagpal, Kunal, Symonds, Matthew, Moran, Melissa, Plass, Markus, Reihs, Robert, Nader, Farah, Tan, Fraser, Cai, Yuannan, Brown, Trissia, Flament-Auvigne, Isabelle, Amin, Mahul B., Stumpe, Martin C., Muller, Heimo, Regitnig, Peter, Holzinger, Andreas, Corrado, Greg S., Peng, Lily H., Chen, Po-Hsuan Cameron, Steiner, David F., Zatloukal, Kurt, Liu, Yun, Mermel, Craig H.
Publikováno v:
Nature Communications Medicine (2021)
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with
Externí odkaz:
http://arxiv.org/abs/2012.05197
Autor:
Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, Mermel, Craig H.
Publikováno v:
Nature Partner Journal Digital Medicine (2021)
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease specific survival for stage II and III colorecta
Externí odkaz:
http://arxiv.org/abs/2011.08965
Autor:
Nabulsi, Zaid, Sellergren, Andrew, Jamshy, Shahar, Lau, Charles, Santos, Edward, Kiraly, Atilla P., Ye, Wenxing, Yang, Jie, Pilgrim, Rory, Kazemzadeh, Sahar, Yu, Jin, Kalidindi, Sreenivasa Raju, Etemadi, Mozziyar, Garcia-Vicente, Florencia, Melnick, David, Corrado, Greg S., Peng, Lily, Eswaran, Krish, Tse, Daniel, Beladia, Neeral, Liu, Yun, Chen, Po-Hsuan Cameron, Shetty, Shravya
Publikováno v:
Nature Scientific Reports (2021)
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligen
Externí odkaz:
http://arxiv.org/abs/2010.11375
Autor:
Wulczyn, Ellery, Steiner, David F., Xu, Zhaoyang, Sadhwani, Apaar, Wang, Hongwu, Flament, Isabelle, Mermel, Craig H., Chen, Po-Hsuan Cameron, Liu, Yun, Stumpe, Martin C.
Publikováno v:
PLOS ONE (2020)
Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving ri
Externí odkaz:
http://arxiv.org/abs/1912.07354
Autor:
Ma, Ruibin, Chen, Po-Hsuan Cameron, Li, Gang, Weng, Wei-Hung, Lin, Angela, Gadepalli, Krishna, Cai, Yuannan
Pathology reports contain useful information such as the main involved organ, diagnosis, etc. These information can be identified from the free text reports and used for large-scale statistical analysis or serve as annotation for other modalities suc
Externí odkaz:
http://arxiv.org/abs/1911.01226
Metadata are general characteristics of the data in a well-curated and condensed format, and have been proven to be useful for decision making, knowledge discovery, and also heterogeneous data organization of biobank. Among all data types in the biob
Externí odkaz:
http://arxiv.org/abs/1909.07846
Autor:
Chen, Po-Hsuan Cameron, Gadepalli, Krishna, MacDonald, Robert, Liu, Yun, Nagpal, Kunal, Kohlberger, Timo, Dean, Jeffrey, Corrado, Greg S., Hipp, Jason D., Stumpe, Martin C.
Publikováno v:
Nature Medicine (2019)
The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samp
Externí odkaz:
http://arxiv.org/abs/1812.00825