Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information, but their role is usually assumed. We propose a causal-reasoning framework for probing covariate dependence in MRI-based International Society of Urological Pathology (ISUP) Grade Group prediction. Rather than treating mpMRI as a direct cause of grade, we model MRI appearance and ISUP grade as observations of latent tumour pathology, and test whether candidate clinical variables act as nuisance correlates, disease-related proxies, or irrelevant covariates in the learned representation. We implement this using ...
| Authors | Yipei Wang, Shiqi Huang, Wen Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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