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Radiomics-Conditioned Modulation of RenalCLIP Features for Clear Cell Renal Cell Carcinoma Classification — arXiv:2609.26492

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26492
License (as archived): CC0-1.0
Archived: 2026-10-08T13:47:17.509Z
SHA-256 of the archived text: 152e9382bc425e5ea3a53137b7f50db8618ba5933af97f88fa3d3c8aec07195e
Stable URL of this page: https://metraipolis.com/documento/dc_7248cb4ec76e

How to cite

Radiomics-Conditioned Modulation of RenalCLIP Features for Clear Cell Renal Cell Carcinoma Classification — arXiv:2609.26492 — arXiv (en), https://arxiv.org/abs/2609.26492 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7248cb4ec76e (Metraipolis archive dc_7248cb4ec76e, sha256 152e9382bc425e5ea3a53137b7f50db8618ba5933af97f88fa3d3c8aec07195e)

BibTeX

@misc{dc7248cb4ec76e,
  title = {Radiomics-Conditioned Modulation of RenalCLIP Features for Clear Cell Renal Cell Carcinoma Classification — arXiv:2609.26492},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26492},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7248cb4ec76e; sha256 152e9382bc425e5ea3a53137b7f50db8618ba5933af97f88fa3d3c8aec07195e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26492. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Radiomics provides quantitative descriptions of tumour appearance that may complement disease-specific foundation models in small labelled cohorts. We investigate this complementarity for computed tomography-based classification of clear cell renal cell carcinoma. Our framework uses radiomics to modulate RenalCLIP features through feature-wise linear modulation (FiLM), while retaining a direct radiomics contribution. Internal testing and external validation compare it with conventional fusion strategies and reference classifiers. The FiLM model achieves an area under the receiver operating characteristic curve (AUC) of 0.804 internally and 0.854 externally, with the highest mean AUC among the evaluated RenalCLIP fusion strategies in both cohorts. Pathway ablations examine the contributions of conditional modulation and the direct radiomics residual, while feature permutation highlights the role of tumour texture. These findings support radiomics as a useful complement to RenalCLIP in a small labelled cohort and identify FiLM as an effective approach to integrating their representations for robust renal tumour classification.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.26492 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:18:38.322Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).