M
Manan Shah
Researcher at Stanford University
Publications - 10
Citations - 365
Manan Shah is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Internal medicine. The author has an hindex of 5, co-authored 7 publications receiving 249 citations.
Papers
More filters
Journal ArticleDOI
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.
Mitko Veta,Yujing J. Heng,Nikolas Stathonikos,Babak Ehteshami Bejnordi,Francisco Beca,Thomas Wollmann,Karl Rohr,Manan Shah,Dayong Wang,Mikael Rousson,Martin Hedlund,David Tellez,Francesco Ciompi,Erwan Zerhouni,David Lanyi,Matheus P. Viana,Vassili Kovalev,Vitali Liauchuk,Hady Ahmady Phoulady,Talha Qaiser,Simon Graham,Nasir M. Rajpoot,Erik Sjöblom,Jesper Molin,Kyunghyun Paeng,Sangheum Hwang,Sunggyun Park,Zhipeng Jia,Eric Chang,Yan Xu,Andrew H. Beck,Paul J. van Diest,Josien P. W. Pluim +32 more
TL;DR: The achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.
Journal ArticleDOI
Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis.
Fred Lu,Suqin Hou,Kristin Baltrusaitis,Manan Shah,Jure Leskovec,Rok Sosic,Jared B. Hawkins,Jared B. Hawkins,John S. Brownstein,John S. Brownstein,Giuseppe Conidi,Julia Gunn,Josh Gray,Anna Zink,Mauricio Santillana,Mauricio Santillana +15 more
TL;DR: It is shown that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions.
Proceedings ArticleDOI
Deep learning assessment of tumor proliferation in breast cancer histological images
TL;DR: This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model.
Posted Content
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
TL;DR: In this article, the authors presented a data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model.
Disease Propagation in Social Networks: A Novel Study of Infection Genesis and Spread on Twitter
TL;DR: A novel pipeline based model is introduced to generate a real-time, accurate depiction of infectious disease propagation using Twitter data that correlates well with theoretical models of infection spread across airport networks, verifying its robustness and applicability in the public sphere.