Uncertainty-Quantified Mamba Architecture for Medical Image Classification
Although deep learning models achieve strong performance in medical image analysis, their clinical adoption is often limited by the lack of reliable uncertainty information and high computational costs. In this work, we propose UQ-Mamba, a novel architecture that integrates uncertainty quantification within state space models. By leveraging the linear-time complexity of Mamba blocks, the proposed approach produces efficient and well-calibrated probabilistic predictions. On the OrganMNIST dataset, UQ-Mamba achieves 89.79% test accuracy with low calibration error (ECE = 0.0202), while providing approximately 3.5× better calibration than ResNet-50 using only 466K parameters. These results demonstrate that UQ-Mamba offers a reliable and efficient solution for resource-constrained clinical environments.