Decouple-and-Couple Learning in Multi-Modal Brain Tumor Segmentation
Bibliographic record
Abstract
Exploiting multi-modal magnetic resonance imaging complementary information for brain tumor segmentation is still a challenging task. Existing methods are usually inclined to learn the joint representation of all tumor regions indiscriminately, thus salient sub-region or healthy tissue would be dominant during the training procedure, which leads to a biased and limited representation performance. In this study, a novel transformer-based multi-modal brain tumor segmentation approach is developed by decoupling and coupling strategy. First, Anatomy-induced Region Decoupler decouples the representation of the tumor scattered in different semantic sub-regions following anatomical view, which forces the model to fully learn intra-region representation separately with multiple modalities context. Additionally, we introduce the collaborative decoupling of the corresponding sub-region edge to serve auxiliary cues. We then design the Edge-supported Intra-region Coupler to separately couple edge and object learning within each anatomical sub-region structure. Lastly, the Mutual Cross-region Coupler is further applied to implement mutual improvement by coupling complementary gains among the above decoupled sub-regions. Extensive experiments clearly demonstrate that our method outperforms current state-of-the-arts for brain tumor segmentation on BRATS2018, BRATS2020, MSD, and BRATS2021 benchmarks while retaining high efficiency in the learning procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".