arXiv AI
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
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Complementary Roles of Radiomics and Foundation Representations in Renal Cell Carcinoma Classification: A Comparative Study of 2D and 3D CT Encodings
arXiv:2609.26463v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced computed tomography remains clinically challenging. R...
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Radiomics--Foundation Fusion for Interpretable RCC Classification: Internal Benchmarking and Exploratory External Transfer
arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...
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Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification
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Radiomics-Conditioned Modulation of RenalCLIP Features for Clear Cell Renal Cell Carcinoma Classification
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Hugging Face Trending Papers
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Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading
Clear cell renal cell carcinoma (CCRCC) grading is essential for treatment planning, yet existing approaches either analyze patch-level images directly or focus solely on nuclei-level classification,...
arXiv AI
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Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading
The paper introduces a semantic‑guided multimodal preprocessing technique that fuses nuclei classification maps with RGB histopathology images for Vision Transformer‑based grading of clear cell renal cell carcinoma. By concatenating classification map channels and applying multiplicative modulation, the method achieves a balanced accuracy of 0.916, markedly surpassing an RGB‑only baseline (0.707) and prior max‑voting approaches (0.427). Sensitivity analysis shows the 21‑percentage‑point improvement remains robust under simulated perturbations matching current nuclei classifier error rates, indicating effective use of imperfect nuclear‑level information.