A modality-agnostic deep learning model reconstructs healthy brain anatomy from pathological CT and MRI scans, trained with fluid-dynamics-based synthetic anomaly generation and contralateral brain symmetry.
Lp-theory for vector potentials and sobolev’s inequalities for vector fields: Application to the Stokes equations with pressure boundary conditions
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
A modality-agnostic deep learning model reconstructs healthy brain anatomy from pathological CT and MRI scans, trained with fluid-dynamics-based synthetic anomaly generation and contralateral brain symmetry.