Progressive multi-scale tumor-state memory for incomplete multimodal brain tumor segmentation
Brain tumor segmentation uses complementary magnetic resonance imaging (MRI) sequences, but some sequences may be unavailable in practice. Their absence changes the information available for segmenting different tumor regions. Fusion methods can support different input subsets. However, fusion at several scales does not by itself specify how tumor evidence is retained and updated to guide fusion at later stages. We propose a progressive multi-scale tumor-state memory network (PMTM) for segmentation from any non-empty subset of T1ce, T1, FLAIR, and T2. PMTM maintains a compact state for the current subject across the encoder stages. At each stage, we use assignment maps to pool spatial fea...
| Authors | Milad Rostamian, Mohammadreza Mohammadi, Mohsen Soryani |
|---|---|
| Primary category | eess.IV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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