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.
(an overview of) synergistic reconstruc- tion for multimodality/multichannel imaging methods.Philosophical Transactions of the Royal Society A, 379(2200):20200205
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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.