M$^{2}$SegMamba: Mamba-Based Incomplete Multimodal Learning for Brain Tumor Segmentation With Few Samples
Bibliographic record
Abstract
The accurate segmentation of brain tumors plays an important role in clinical diagnosis and treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and complementary information for accurate brain tumor segmentation. However, the common problems of incomplete modalities and small samples in clinical practice seriously affect the performance of multimodal segmentation. In this work, we design a new framework, named M$^{2}$SegMamba, using Mamba and Masked Autoencoder networks for both supervised and self-supervised learning, aimed at handling small sample brain tumor segmentation under various incomplete multimodality settings. We construct a masking strategy suitable for multimodal brain tumors to precisely extract image features, which serves as the foundation for image segmentation. By fully leveraging the capabilities of the Mamba network, we design a multi-traversal method to facilitate the interaction between inter-modal and cross-modal image features. Meanwhile, the introduction of TSmamba in skipping connections efficiently integrates multimodal features. Auxiliary regularizers are introduced in both the encoder and decoder to further enhance the model's robustness to incomplete modalities. We conducted experiments on the BraTS 2018 and BraTS 2020 datasets, and the results demonstrate that our method outperforms state-of-the-art brain tumor segmentation methods on most subsets of missing modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".