CGCP: Clinically-Guided Contrastive Pretraining with Mixed-Type Clinical Signals for 3D Brain MRI

Neuroimaging cohorts often pair brain MRI with structured clinical assessments, yet representation pretraining either ignores these signals or incorporates them without establishing whether they provide information beyond the downstream diagnostic target. We introduce Clinically-Guided Contrastive Pretraining (CGCP), a representation-learning framework for 3D brain MRI that integrates heterogeneous clinical supervision through continuous cognitive-severity learning, discrete stage-aware contrastive learning with a label-tracking momentum memory bank, and variance regularisation. A Bayesian optimisation procedure balances these objectives while retaining a minimum contribution from clinically meaningful staging supervision. Beyond proposing a pretraining strategy, we use CGCP as an analytical probe to determine when mixed-type clinical supervision provides genuinely complementary information. We evaluate longitudinal-to-cross-sectional transfer against label-free self-supervised, supervised, and label-aware alternatives under subject-level evaluation, complemented by targeted loss ablations, frozen and nonlinear probing, layer-wise analysis, calibration, data-efficiency experiments, and sensitivity studies. The resulting evidence shows that clinically guided representations can support strong downstream classification, but their benefit is not uniform: correlated clinical instruments can act as label proxies, measurement ceilings can restrict continuous supervision, representation rankings can change substantially after fine-tuning, and memory-constrained optimisation can complicate multi-objective balancing. We consolidate these observations into a validity framework for evaluating clinically guided representation learning before improvements are attributed to additional clinical supervision.