Cong Gao

@CongGaoJHU

Computer Science PhD candidate @ Johns Hopkins University

Joined April 2019

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  1. Retweeted
    19 Nov 2021
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  2. Retweeted
    12 Oct 2021

    Check out Stereo Transformer at , which revisists the stereo depth estimation from a sequential problem. We will be there today and Thursday for Q&A. Many thanks to my collegues/advisor and more!

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  3. Retweeted
    6 Aug 2021

    The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective by et al. including

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  4. 20 Jul 2021

    My journal article regarding fluoroscopic navigation for a continuum robot is recently accepted at TBME! It is now early accessible via IEEE Xplore:

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  5. Retweeted

    Invited talk @ MCV Workshop -> Self-supervised Learning and 3D Vision Under Photometric Incosistency for Quantitative Endoscopy by Prof. Mathias Unberath (Johns Hopkins University). In conjunction with / 2021. More info:

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  6. 13 Feb 2021

    Integrating C-arm image guidance for robotic assisted femoroplasty

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  7. 7 Oct 2020

    I will present my work at shortly in the 11am EST Image registration C session. Check out the oral video here: and the proceedings:

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  8. Retweeted
    5 Oct 2020

    Great start to with an award at AE-CAI for my then-first-year PhD student, Wenhao. TL/DR: If you plan on using short throw depth sensor for image-based registration during navigation you're probably in bad luck.

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  9. Retweeted
    4 Apr 2020
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  10. Retweeted
    2 Apr 2020

    Our paper, "A County Level Dataset for Informing the United States' Response to COVID-19" is available here: . The figure shows a spike in grocery store visits when the White House declared a national emergency.

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  11. Retweeted
    2 Apr 2020

    Our County-level Summaries dataset is now live on . We've added new data, eg COVID19 time-series from the dashboard and out-of-home activity aggregated from . Please use and share:

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  12. Retweeted
    30 Mar 2020

    We extend spatial transformers to projective geometry for diff-able volume rendering. In 2D/3D reg, this allows learning of an image similarity function that is convex w.r.t. to pose params. Git: arXiv: @Cong83839828

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  13. Retweeted
    25 Jan 2020

    We're going to ! Jie Ying Wu et al., Data-driven error correction for soft-tissue simulations (in robotic surgery), and Rob Grupp et al., Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration (demo below)

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