Multi-cancer framework with cancer-aware attention and adversarial mutual-information minimization for whole slide image classification
Bibliographic record
Abstract
• Novel cross-cancer framework improves generalization across tumor types. • Cancer-Aware VisionTransformer extracts shared and cancer-specific features. • Adversarial regularization for enhancing universal features. • Hierarchical balancing addresses data imbalances for unbiased learning. • Benchmark dataset combines multiple cancer types to support multi-cancer digital pathology. Whole Slide Images (WSIs) are crucial in modern pathology, offering high-resolution data for accurate diagnosis, treatment planning, and research. Deep learning methods have recently been proposed to harness this data by extracting and interpreting complex patterns. However, these approaches often focus on specific tumor types, limiting their generalizability across diverse pathological conditions and restricting scalability. This relatively narrow focus ultimately stems from the inherent heterogeneity in histopathology and the diverse morphological and molecular characteristics of different tumors. To this end, we propose a novel approach for multi-cancer WSI analysis, designed to leverage the diversity of different tumor types. We introduce a Cancer-Aware Attention module that models both shared patterns across cancers and cancer-specific variations to address heterogeneity and enhance cross-tumor generalization. Furthermore, we construct an adversarial cancer regularization mechanism to minimize cancer-specific biases through mutual information minimization. Additionally, we develop a hierarchical sample balancing strategy to mitigate data imbalances and promote unbiased learning. Together, these form a cohesive framework for unbiased multi-cancer WSI analysis. Extensive experiments on a uniquely constructed multi-cancer dataset demonstrate significant improvements in generalization, providing a scalable solution for WSI classification across diverse cancer types.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".