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arXiv AI By Fatemeh Javadian, Zhu Chen, Zahra Aminparast, Johannes Stegmaier

Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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