BlindHarmonyDiff converts MR images from unseen scanners to a target scanner style using a 3D edge-to-image rectified flow trained only on target data, plus a refinement step that preserves source structure.
Auto- mated classification of alzheimer’s disease and mild cogni- tive impairment using a single mri and deep neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Efficient and robust 3D blind harmonization for large domain gaps
BlindHarmonyDiff converts MR images from unseen scanners to a target scanner style using a 3D edge-to-image rectified flow trained only on target data, plus a refinement step that preserves source structure.