arXiv：cs.AI· Yuan Liang, Morgan May, Abraham Campbell, Sourav Bhattacharjee, Simon Caton, Pierpaolo Dondio·· 16 小时前
在肾细胞癌分类中评估放射组学-图像融合的概率校准
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
AI 导读
研究基于 KiTS23 数据集的 396 名患者，检验在四类图像模型中引入放射组学能否在区分度之外提升概率预测质量。在 12 组匹配对比中，AUC 和 AP 全部 12 组提升，Brier score 与 NLL 提升 11 组，ECE 提升 8 组；3D MedVAE 门控融合模型综合表现最佳，概率平均集成在全部五个主指标上均优于纯图像模型。
正文
Abstract:Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma (RCC) are commonly evaluated primarily by discrimination. Using 396 patients from the KiTS23 dataset, we evaluated whether adding radiomics to four image-model families improved probabilistic performance in addition to discrimination. Seventeen individual models and four probability-average ensembles were assessed using discrimination, probability calibration, and proper scoring measures. Across 12 matched image-only versus fusion comparisons, AUC and AP improved in all 12, while Brier score and negative log likelihood (NLL) improved in 11 of 12 and expected calibration error (ECE) in eight of 12. The 3D MedVAE gated-fusion model achieved the strongest overall individual-model profile. Probability averaging across concatenation, cross-attention, and gated fusion produced favourable point estimates across all five primary metrics relative to image-only models in all four families, although paired-bootstrap support was stronger for Brier score and NLL than for ECE. These findings show that radiomics--image fusion can improve probabilistic prediction quality beyond discrimination, while calibration behaviour varies across image architectures and fusion strategies. We show that our proposed probability-average ensemble models improve discrimination and calibration simultaneously across all architectures in the study.
| Comments: | 12 pages. 1 figure. Accepted at the 2026 Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2026). Proceedings forthcoming |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2610.14151 [eess.IV] |
| (or arXiv:2610.14151v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14151 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuan Liang [view email]
[v1]
Fri, 9 Oct 2026 15:50:47 UTC (71 KB)
来源：arXiv：cs.AI · arxiv.org