Using deep image prior to assist variational selective segmentation deep learning algorithms

Liam Burrows; Ke Chen; Francesco Torella

doi:10.1117/12.2606212

10 December 2021 Using deep image prior to assist variational selective segmentation deep learning algorithms

Liam Burrows, Ke Chen, Francesco Torella

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120880S (2021) https://doi.org/10.1117/12.2606212
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Variational segmentation algorithms require a prior imposed in the form of a regularisation term to enforce smoothness of the solution. Recently, it was shown in the Deep Image Prior work that the explicit regularisation in a model can be removed and replaced by the implicit regularisation captured by the architecture of a neural network. The Deep Image Prior approach is competitive, but is only tailored to one specific image and does not allow us to predict future images. We propose to incorporate the ideas from Deep Image Prior into a more traditional learning algorithm to allow us to use the implicit regularisation offered by the Deep Image Prior, but still be able to predict future images.

Citation Download Citation

Liam Burrows, Ke Chen, and Francesco Torella "Using deep image prior to assist variational selective segmentation deep learning algorithms", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120880S (10 December 2021); https://doi.org/10.1117/12.2606212

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