UMGen: Multi-scale and Multi-region Joint Prediction Pipeline for Uveal Melanoma Prognosis-related Gene Subtyping
Bibliographic record
Abstract
Uveal Melanoma(UM) is a highly aggressive ocular malignancy. Once metastasis occurs, the survival period is very short. An effective prognosis for UM is necessary. The BAP1 gene and Somatic Copy Number Alterations (SCNA) are two of the important indicators for clinical UM prognosis. However, current methods for detecting prognosis-related genes have limitations, including high costs and the need for advanced technical expertise, which makes them challenging to apply in routine clinical practice. Low-cost and efficient prognosis-related gene detection is worth studying. In this paper, we investigate the prognosis-related gene subtyping prediction problem based on Whole Slide Images(WSI). We propose a novel method, UMGen, for WSI-based UM prognosis-related gene subtyping prediction. Specifically, UMGen consists of a multi-region sampling module, a classifier module, and a joint decision module. The classifier module, SAGNet, applies spatial attention to extract multi-scale features. To improve accuracy and generalization, the multi-region joint decision is introduced. Comprehensive quantitative ablation experiments demonstrate that both our UMGen and SAGNet can surpass other competitors and have excellent generalization and robustness. The code and models will be released to the public for further research.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".