SPLIT partitions projection data to enforce cross-consistency and measurement fidelity, proving that its self-supervised objective matches supervised training in expectation under mild conditions, with strong results on sparse-view multispectral CT.
Improving spectral ct image quality based on channel correlation and self-supervised learning.IEEE Transactions on Computational Imaging, 9:1084–1097, 2023
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SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
SPLIT partitions projection data to enforce cross-consistency and measurement fidelity, proving that its self-supervised objective matches supervised training in expectation under mild conditions, with strong results on sparse-view multispectral CT.