Q-Capsformer: A Quantum-Swarm-Optimized Capsule–Transformer Framework With Pareto-Calibrated Uncertainty For Automated Diagnosis Of Acute Lymphoblastic Leukemia In Digital Pathology

Abstract

One small step toward this goal would be to validate the family of models in use for the setting of acute lymphoblastic leukemia (ALL), which is the most common pediatric malignancy, accounts for approximately one-quarter of all childhood cancer cases worldwide, and still largely relies on traditional microscopic diagnosis which suffers inter-observer variability ranging from 18–23%, examination times as long as six to eight hours per specimen, and a fatigue-induced error rate that can approach 15% after prolonged review. Such constraints provide the impetus for diagnostic support systems that are accurate, computationally efficient, robust to staining variation and transparent about their predictive confidence. In this paper, we present Q-CapsFormer: a quantum-swarm-optimized capsule–transformer framework that integrates seven heterogeneous modules, including: adaptive stain normalization; dynamic-routing capsule networks for part-whole cellular representation; quantum-inspired particle swarm optimization with Lévy flight (QPSO-LF) architecture search; multi-objective Bayesian optimization (MOBO), driven by Expected Hypervolume Improvement, for Pareto-optimal model selection; Swin Transformer backbone in terms of shifted-window attention mechanism to capture the global context; supervised contrastive learning with an adaptive hard-negative mining framework and a dual-purpose Monte Carlo Dropout/deep-ensemble module for calibrated uncertainty estimation and multi-scale blast localization.

Through stratified five-fold, patient-disjoint cross-validation on four public benchmarks (ALL-IDB1, ALL-IDB2, C-NMC and LISC; 26,303 images in total), the proposed framework reaches a mean classification accuracy of 99.2%, F1-score of 98.9%, specificity of 99.3% and mean IoU of 0.972—significantly outperforming fifteen state-of-the-art baselines by margins between +2.7%–4.2% while maintaining real-time inference at an average time of 23.6 ms per image (42.3 FPS for normalized dimensions) @512×​512 tiles.] Our quantum-inspired optimizer converges 3.4× faster than standard PSO and the uncertainty-module achieves an Expected Calibration Error of 0.024, allowing automated reporting for 89.3% of cases at a selective accuracy of 99.7% with the rest being routed for expert review. Taken together, these results suggest that jointly optimizing for representation learning, architecture search and predictive calibration achieves a diagnostic pipeline that is simultaneously more accurate, faster and clinically interpretable than single-objective deep learning baselines.