F3-Net combines multi-encoder nnU-Net with zero-filled missing modalities to segment glioma, metastasis, stroke, and white matter lesions, but the missing-modality claim is untested and comparisons are incomplete.
Deep learning for image en- hancement and correction in magnetic resonance imaging—state-of-the-art and challenges,
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
cs.CV 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement
F3-Net combines multi-encoder nnU-Net with zero-filled missing modalities to segment glioma, metastasis, stroke, and white matter lesions, but the missing-modality claim is untested and comparisons are incomplete.