Prof. Dr. Bangti Jin

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Prof. Dr. Bangti Jin

Ehemaliger Mitarbeiter der AG Technomathematik

E-Mail: B.Jin (at) cs.ucl.ac.uk
Persönliche Homepage: http://www0.cs.ucl.ac.uk/people/B.Jin.html

Electrical Impedance Tomography with the complete electrode model and sparsity constraints (Matthias Gehre)

R. Barbano, A. Denker, H. Chung, T. H. Roh, S. Arridge, P. Maaß, B. Jin, J. C. Ye.
Steerable conditional diffusion for out-of-distribution adampation in medical image reconstruction.
IEEE Transactions on Medical Imaging, 44(5), 2093:2104, 2025.
DOI: 10.1109/TMI.2024.3524797
A. Denker, F. Margotti, J. Ning, K. Knudsen, D. Nganyu Tanyu, B. Jin, A. Hauptmann, P. Maaß.
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography.
Zur Veröffentlichung eingereicht.
DOI: 10.48550/arXiv.2508.06281
J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Transactions on Machine Learning Research, 12, 2023.
online unter: https://openreview.net/forum?id=FWyabz82fH
R. Barbano, J. Antorán, J. Leuschner, J. M. Hernández-Lobato, B. Jin, Z. Kereta.
Image Reconstruction via Deep Image Prior Subspaces.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2302.10279
M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.

online unter: https://2023.midl.io/papers/p014