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Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading
Not specified in the provided content
cross-modalcontrastive learningmedical imagingcancer grading
2609.26920
Builder Relevance
2h ago70%
Abstract
A cross-modal contrastive learning framework, RCC-Align, improves noninvasive CT-based grade prediction for clear cell renal cell carcinoma by aligning histopathology and CT data.
Reality Card
Core Claim
RCC-Align outperforms CT-only baselines in low-grade ccRCC prediction while requiring only CT data at inference.
Method / Result
Achieved an AUC of 0.601 and significantly improved low-grade prediction (p = 0.004).
Limitations
Validation in larger, multi-institutional cohorts with external testing is needed before clinical translation.
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