A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
Niakan Kalhori
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.
citing papers explorer
-
Generative Modeling of Complex-Valued Brain MRI Data
A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
-
Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.