Abstract
Clear cell renal cell carcinoma (CCRCC) grading is essential
for treatment planning, yet existing approaches either analyze patchlevel
images directly or focus solely on nuclei-level classification, without
linking to final tumor grading. We propose a semantic-guided multimodal
preprocessing method that integrates nuclei classification maps
from existing pre-trained models with RGB histopathology images for
Vision Transformer (ViT)-based CCRCC grading. Our approach employs
classification map channel concatenation and multiplicative modulation,
with optimized overlays to leverage nuclei grading information, while
preserving RGB textural features. Evaluation of multiple preprocessing
strategies demonstrates that semantic-guided enhancement achieves
0.916 balanced accuracy, outperforming RGB-only baseline (0.707) and
max-voting aggregation from prior studies (0.427). Sensitivity analysis
reveals that this 21 percentage point improvement over baseline persists
even under simulated perturbation at rates matching current state-ofthe-
art nuclei classification model error thresholds, suggesting both effective
semantic utilization and practical robustness. These findings show
that preprocessing-based multimodal fusion can leverage the diagnostic
potential of existing imperfect nuclei classifiers, effectively bridging previously
isolated fine-grained nuclear-level analysis with coarse-grained ViTbased
patch classification. Per-class recall was consistent across grades
(0.93, 0.91, 0.91), indicating that gains are not concentrated in the majority
class. Because the sensitivity analysis perturbs ground-truth maps
rather than predictions from an actual nuclei model, this result characterizes
robustness under simulated error rather than deployment with a
real upstream model, which remains for future work.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COMPAYL_066.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
BibTex
@InProceedings{JavFat_SemanticGuided_MICCAISAT2026,
author = { Javadian, Fatemeh AND Chen, Zhu AND Aminparast, Zahra AND Stegmaier, Johannes},
title = { { Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17251},
month = {pending},
page = {pending}
}