UniSegDiff uses staged training and inference with alternating mask/noise prediction targets plus STAPLE fusion of multiple samples to reach state-of-the-art lesion segmentation across six datasets and modalities.
Data in brief28, 104863 (2020)
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UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
UniSegDiff uses staged training and inference with alternating mask/noise prediction targets plus STAPLE fusion of multiple samples to reach state-of-the-art lesion segmentation across six datasets and modalities.