Quantum-Assisted Prototype-Guided Refinement for Robust Medical Image Classification under Label Noise

Labels of medical images are often corrupted by annotation errors, diagnostic ambiguity, and uncertainty in report-derived findings, and deep networks trained on such labels memorize the errors. Prototype-based label correction can repair corrupted supervision, but only in an embedding where classes are separated and concentrated; otherwise erroneous assignments contaminate the prototypes and reinforce the errors they are meant to correct. We propose HQProNet, which couples trusted-anchor prototype refinement with a compact variational quantum circuit (VQC) that defines the matching geometry. Prototypes are initialized from a small audited anchor set, temperature-scaled cosine similarities yield refined soft targets, and confidence-gated moving-average updates adapt the prototypes during training. The VQC maps a unit-norm 256-dimensional amplitude vector to eight Pauli-$Z$ expectations, each a trainable quadratic form with eigenvalues in $[-1,1]$, using 120 rotation angles. A conditional analysis shows that prototype matching recovers the clean label when the number of trusted anchors, the within-class covariance, and the cosine margin jointly satisfy an explicit condition. On six MedMNISTv2 datasets under symmetric and pairwise corruption, HQProNet attains the best macro-F1 in 34 of 36 conditions, and on CheXpert it is best on all six metrics under two uncertainty policies. With the prototype mechanism fixed, replacing the VQC by linear, nonlinear, or parameter-matched classical projectors lowers macro-F1 by at least 2.2 points in every setting and reduces the cosine margin and refinement accuracy: prototype refinement is the primary source of robustness, and the VQC supplies the most favorable geometry among the projectors tested.