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· 0 citations
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 tomography (PET), or ultrasound (US), offers previously unattained opportunities for diagnosis, therapy planning, and therapy assessment. Medical image processing is essential to leverage this increasing amount of data and to explore and present the contained information in a way suitable for the specific medical task. In this tutorial, we will approach the analysis and visualization of medical image data in an explorative manner. In particular, we will visually construct the image processing algorithms using the popular graphical data-flow builder MeVisLab, which is available as a free download for noncommercial research. We felt that it could be more interesting for the reader to see and explore examples of medical image processing that go beyond simple image enhancements. The part of exploration, to inspect medical image data and experiment with image-processing pipelines, requires software that encourages this kind of visual exploration.
Lei Mou, Yitian Zhao, H. Fu et al.· IEEE Pulse· 398 citations· ⚡29
Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches primarily rely on probability outputs from trained models (point predictions), which provide no formal guarantees on prediction coverage and often require additional calibra- tion techniques to improve reliability. In contrast, conformal prediction (region prediction) offers a principled alternative by generating prediction sets with finite- sample validity guarantees, ensuring that the ground truth is contained within the set at a specified confidence level. In this study, we explore the impact of pre-training approach, dataset scale and domain on both point and region-level uncertainty quantification, by studying domain-specific vision medical foundation models vs. general domain vision foundation models. We conduct a comprehensive evaluation across foundation models trained on retinal, histopathological, and Chest X-Rays data, applying various calibration techniques. Our results demonstrate that (1) pre-training on higher-quality domain-specific datasets along with self-supervised learning leads to better-calibrated point predictions than general domain pre-training, (2) stan- dard re-calibration methods alone cannot fully mitigate uncertainty discrepancies across models trained on different data sources, (3) domain-specific foundation model can lead to more efficient conformal prediction. These findings highlight the importance of careful model selection and the inte- gration of both point and region prediction to enhance the reliability and trust- worthiness of medical AI systems. Our 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.
OBJECTIVE
Recently, deep learning models in medical imaging have undergone tremendous advancements. However, these deterministic discriminative models often tend to overfit and produce overconfident predictions. To explore a more efficient strategy, we propose 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. The proposed method inherently encodes aleatoric uncertainty by sampling features from multiple denoising time steps, which is then used to regularize prediction during training via entropy regularized loss.
RESULT
We conducted extensive experimentation over five diverse clinical datasets, and our framework demonstrates competitive performance with reduced prediction entropy and improved robustness to input degradation. The negative entropy-accuracy correlation, expected calibration error, and dynamic uncertainty demonstrate the effectiveness of the proposed framework in classification tasks. Our framework achieved competitive performance compared with traditional convolutional neural networks and vision transformers with an AUC of 83.81%, 97.45%, 86.15% and 96.75% on datasets 1, 2, 3 and 4, respectively. The reduced ECE values on the datasets demonstrate the calibrated performance of the proposed method.
Jutika Borah, Rajkumar Saini, A. Fayjie et al.· BMC Research Notes· 0 citations
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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