Learned image reconstruction for large scale tomographic imaging

With Professor Andreas Hauptmann

Learned image reconstruction for large scale tomographic imaging

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.

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Excited to announce that Mark Girolami will give a talk on 'The Statistical Finite Element Method' at the second CC… https://t.co/LUjU07ZMIz View on Twitter