University of Cambridge > Talks.cam > CMIH Hub seminar series > Learned image reconstruction for large scale tomographic imaging

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Learned image reconstruction for large scale tomographic imaging

Download to your calendar using vCal

If you have a question about this talk, please contact J.W.Stevens .

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate high quality images with a considerable speed-up compared to classical reconstruction methods. This is especially true for model-based learned (iterative) reconstruction schemes. However, applicability to large scale inverse problems is limited by available memory for training and extensive training times. In this talk I will discuss applicability of learned image reconstruction approaches to tomographic data. In particular we will discuss various imaging scenarios and modalities, suitable approaches to design a robust learning task, as well as some solutions to obtain scalable learned image reconstruction for large scale and high dimensional data.

This talk is part of the CMIH Hub seminar series series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

MR11, CMS, Wilberforce Road, Cambridge, CB3 0WB Trinity Hall, Fellows Garden SG1 & SG2, Alison Richard Building, 7 West Rd, Cambridge CB3 9DT

Other talks

"Gods, Monsters & Middle-class Uncles: Indian Comics in a Time between Worlds" Spectral theory for Dirac operators with singular potentials Director's Welcome A scientists guide to the art of radiation therapy and Neurosurgery for the oncologist The Neurophenomenology of Surrender: Agency, Renewal, and Social Engagement in Speaking in Tongues Title TBC
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity