Spherical Procrustes Alignment for Reliable Medical Audio Diagnosis

Reliable medical audio diagnosis requires models that are both accurate and honest about their uncertainty. However, fine-tuned models on small, imbalanced datasets often become overconfident due to norm bias, where predictions rely on feature magnitude rather than semantic alignment. The Equiangular Tight Frame (ETF) provides a theoretical optimum for class separation and is effective for imbalanced and calibration tasks due to its maximal angular separability and geometric fairness, but existing ETF-based methods perform poorly on noisy medical data because gradient rotation is unstable and fixed ETFs cannot adapt to drifting prototypes. To address this, we propose Spherical Procrustes Alignment (SPA), which combines spherical constraints with dynamic ETF alignment. SPA uses a Spherical branch to eliminate norm bias via normalization and a Geometric branch to adapt features and align a fixed ETF with drifting prototypes via dynamic Procrustes alignment, while a self-alignment mechanisms fuses the two branches to jointly optimize logits. Experiments on ICBHI 2017 and CirCor DigiScope show that SPA achieves state-of-the-art performance and transforms pre-trained models into reliable and efficient clinical tools without extra inference cost.