Complementary Roles of Radiomics and Foundation Representations in Renal Cell Carcinoma Classification: A Comparative Study of 2D and 3D CT Encodings

arXiv:2609.26463 · cs.CV, cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: Accepted at Medical Image Understanding and Analysis (MIUA 2026). 15 pages, 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 21× in this paper · explained in 2 episodes
• Interpretability — 16× in this paper · explained in 15 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Dynamic Weighting — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Med3D: Transfer Learning for 3D Medical Image Analysis
• Gated Multimodal Units for Information Fusion
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Towards A Rigorous Science of Interpretable Machine Learning

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