Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 12 references
Abstract
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.
This work proposes an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning, employing a 2D-adapted selective state-space mechanism to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling.
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
Seyed Sina Ziaee, K. Ovens· Journal of Imaging· 1 citation
Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tom...
Lei Mou, Yitian Zhao, H. Fu et al.· IEEE Pulse· 398 citations· ⚡29
This work underscores the need for a holistic approach to uncertainty quantification in recent development of medical vision foundation model, ensuring robust and interpretable AI-driven decision-making and highlights the importance of careful model selection and the inte- gration of both point and region prediction to...
A diffusion-conditioned representation learning that leverages the internal dynamics of a diffusion model to extract noise-level awareness and diverse semantic representations in disease classification and detecting distributional shifts is proposed.
Jutika Borah, Rajkumar Saini, A. Fayjie et al.· BMC Research Notes· 1 citation
This study introduces a hybrid quantum– classical deep learning framework that leverages quantum variational filtering in tandem with transfer learning via ViT-B16 and EfficientNet-B3 to enhance feature representation and demonstrates promising robustness and generalization in limited-data environments.
S. Behuria, Sujata Swain, Anjan Bandyopadhyay et al.· Artificial Intelligence in H...· 0 citations
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