Cross-Attention Patch Fusion for Few-Shot Colorectal Tissue Generation
Bibliographic record
Abstract
Labeled colorectal histopathology is scarce, especially for rare patterns, which limits robust training and validation. We target patch-level classes (e.g., normal epithelium, tumorous glands, stroma) and propose Cross-Attention Patch Fusion, a few-shot generator that synthesizes class-conditioned tissue patches from only a handful of expert-labeled seeds. A base patch attends to k references to find semantically matched local blocks; these blocks are fused in feature space and reweighted with channel-and-spatial attention to preserve gland boundaries, epithelial contours, stromal context, and texture transitions. On NCT-CRC-HE, our method reduces Fréchet Inception Distance (FID) by 4.7%, raises LPIPS by 22.0% (to 58.57), and improves few-shot classifier accuracy by 4.8 percentage points when trained with our synthetic data. Qualitative results show realistic gland architecture and plausible stromal textures. In sum, cross-attention plus patch-wise fusion yields more realistic and diverse colorectal tissue patches from few examples and supplements rather than replaces expert labels.Clinical relevance— By generating realistic, diverse colorectal tissue images from only a few examples, our model can help train and validate AI diagnostic tools without the need for extensive manual annotation. This may accelerate development of automated classifiers for rare histological patterns, supporting earlier and more accurate detection of colorectal lesions in clinical practice
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".