Uncertainty Quantification (UQ) is critical for detecting hallucinations in black-box Large Vision-Language Models (LVLMs). However, prevailing methods like Discrete Semantic Entropy (DSE) are unreliable, as their scores are primarily dominated by the number of semantic clusters. This renders them incapable of distinguishing between benign semantic ambiguity (varied but coherent responses) and severe belief conflict (contradictory responses). We address this limitation by proposing a novel framework rooted in Dempster-Shafer theory of evidence, built on the premise that not all inconsistency is equal. Our method decomposes uncertainty into two complementary metrics: Belief Divergence, which quantifies ambiguity by measuring the separation between viewpoints, and Belief Conflict, which captures direct logical contradictions. Extensive experiments demonstrate that our framework provides a more reliable measure of uncertainty.

The fusion of multi-view medical images through deep neural networks is essential for boosting diagnostic precision in the field of medical image analysis. However, the reliability of these diagnostic results is often compromised by imperfections in image views, manifested as noise, artifacts, and data deficits arising from inconsistent diagnostic frequencies. These issues introduce a significant risk when merging medical views in a clinical setting. To address these problems, we introduce the Reliability-Enhanced Multi-view Network (REMNet), a novel framework designed to tackle two critical challenges: 1) reducing misclassification and uncertainty from imperfect view integration, and 2) improving the reliability and interpretability of multi-view medical image predictions. Specifically, REMNet merges information from multiple views into a coherent evidence framework and incorporates a Dirichlet prior within our predictive model to more accurately estimate confidence in predictions. Coupled with a robust fusion strategy and a precise confidence calibration process, REMNet consolidates the diverse strengths of various medical imaging views, reduces the impact of view imperfections, and enhances the reliability of medical imaging diagnostics. The superiority of REMNet is validated through comprehensive theoretical analysis and empirical experiments on multi-view medical image datasets across different modalities.

Open World Object Detection (OWOD) faces a fundamental dilemma: maintaining a stable representation for known classes while reserving flexible space for discovering unknown objects. Existing methods, while improving recall, often fail to assign discriminative confidence scores to unknown instances, resulting in critically low Average Precision (U-AP) and representation degradation during incremental learning. To remedy this, we propose the Evidential Prior Guided Neural Collapse (ENC) framework. ENC unifies representation learning and uncertainty quantification via a Geometric-Evidence Coupling mechanism. Unlike previous approaches, we map evidential support directly to the angular alignment with Simplex Equiangular Tight Frame (ETF) prototypes. Theoretically, the evidential prior functions as a geometric regularizer: it maximizes equiangular separation for confident known samples, while constraining ambiguous queries to approximate an isotropic uniform distribution via distributional regularization. Furthermore, to mitigate decision conflicts in self-supervised learning, we propose a dissonance-aware objectness optimization strategy that mines informative samples near the decision boundary. Extensive experiments on M-OWODB and S-OWODB benchmarks demonstrate that ENC sets a new state-of-the-art. Notably, it achieves a significant improvement in unknown class discovery, boosting U-AP from ≈ 1% to 9.2%, while exhibiting superior robustness against catastrophic forgetting in challenging incremental scenarios.

Multi-view classification based on evidence theory aims to enhance result reliability by effectively quantifying prediction uncertainty at the evidence level, particularly when dealing with low-quality views. However, these methods face limitations in real-world applications due to the sensitivity of estimated uncertainty to view distribution, leading to two main issues: 1) difficulty in making clear judgments about whether to trust predictions based on vague uncertainty scores, and 2) the potential negative impact of integrating information from low-quality views on multi-view classification performance. Both limitations compromise the reliability of multi-view decisions. To address these challenges, we introduce an adaptive rejection mechanism based on estimated uncertainty, which is free of data distribution constraints. By integrating this adaptive rejection mechanism into the fusion of multiple views, our method not only indicates whether predictions should be adopted or rejected at the view level but also enhances classification performance by minimizing the impact of unreliable information. The effectiveness of our method is demonstrated through comprehensive theoretical analysis and empirical experiments on various multi-view datasets, establishing its superiority in enhancing the reliability of multi-view classification.

Clinical disease classification naturally follows a hierarchical reasoning process, where clinicians first distinguish coarse diagnostic categories (e.g., benign vs. malignant) before identifying specific subtypes to enhance diagnostic accuracy. While multi-view imaging offers complementary perspectives, most existing machine learning approaches perform flat decision fusion, ignoring this intrinsic hierarchy and handling inter-view conflicts and uncertainty primarily at the decision-fusion level. To address this gap, we propose the Hierarchical Multi-view Evidence Aggregation Network (H-MEAN), an end-to-end architecture that explicitly mimics clinical decision-making process. H-MEAN consists of three key modules: (i) Multi-View Fusion, which jointly encodes heterogeneous imaging views to capture complementary features; (ii) Hierarchical Tree Alignment, which structurally propagates diagnostic cues across granularity levels to enforce clinical consistency; and (iii) Hierarchical Evidential Aggregation, which leverages evidential deep learning to quantify uncertainty throughout the diagnostic hierarchy. Extensive experiments on two multi-view medical datasets demonstrate that H-MEAN consistently outperforms competitive methods, achieving higher classification accuracy, particularly in challenging settings with missing or noisy views.

Trusted multi-view classification (TMVC) addresses variations in data quality by evaluating the reliability of each view based on prediction uncertainty at the evidence level, reducing the impact of low-quality views commonly encountered in real-world scenarios. However, existing TMVC methods often struggle to maintain robustness during testing, particularly when integrating noisy or corrupted views. This limitation arises because the evidence collected by TMVC may be unreliable, frequently providing incorrect information due to complex view distributions and optimization challenges, ultimately leading to classification performance degradation. To enhance the robustness of TMVC methods in real-world conditions, we propose a generalized evidence filtering mechanism that is compatible with various fusion strategies commonly used in TMVC, including Belief Constraint Fusion, Aleatory Cumulative Belief Fusion, and Averaging Belief Fusion. Specifically, we frame the identification of unreliable evidence as a multiple testing problem and introduce p-values to control the risk of false identification. By selectively down-weighting unreliable evidence during testing, our mechanism ensures robust fusion and mitigates performance degradation. Both theoretical guarantees and empirical results demonstrate significant improvements in the classification performance of TMVC methods, supporting their reliable application in challenging, real-world environments.

Dental caries is a common oral disease, and accurate imaging is vital for diagnosing, especially for assessing carious lesions and the pulp area. Cone-Beam Computed Tomography (CBCT) is a widely used imaging technology for the clinical diagnosis of caries, but its low resolution and blurred boundaries hinder clear visualization of the pulp and lesion extent. Micro Computed Tomography (Micro-CT) provides higher resolution and precision, but it’s only used for ex vivo imaging. Recently, image synthesis techniques have been extensively applied to enhance the quality of medical images. However, existing synthesis methods often focus on pixel-level correspondence, neglecting the semantic information within the images. This results in limitations in reconstructing structural edges and accurately simulating anatomical regions. To address these challenges, we propose a novel model that generates Micro-CT images from CBCT images. The proposed Semantic-Aware Synthesis Network (SASN) employs a multi-task learning strategy, integrating a segmentation task to enhance the information learning capability of the shared encoder, thereby facilitating synthesis. To achieve better semantic-based synthesis, we employ a Semantic-Guided Attention Module (SGAM) to facilitate feature fusion between branches. Additionally, we introduce a semantic alignment loss to ensure semantic consistency, thereby further enhancing network performance. Experimental results demonstrate that SASN outperforms other existing methods, achieving 27.05 (± 0.18) in PSNR, 84.41[%] (± 0.97) in SSIM, and 45.47 [×1e-3] (± 1.37) in RMSE.

Uncertainty-aware multi-view deep classification methods have markedly improved the reliability of results amidst the challenges posed by noisy multi-view data, primarily by quantifying the uncertainty of predictions. Despite their efficacy, these methods encounter limitations in real-world applications: 1) They are limited to providing a single class prediction per instance, which can lead to inaccuracies when dealing with samples that are difficult to classify due to inconsistencies across multiple views. 2) While these methods offer a quantification of prediction uncertainty, the magnitude of such uncertainty often varies with different datasets, leading to confusion among decision-makers due to the lack of a standardized measure for uncertainty intensity. To address these issues, we introduce Conformalized Multi-view Deep Classification (CMDC), a novel method that generates set-valued rather than single-valued predictions and integrates uncertain predictions as an explicit class category. Through end-to-end training, CMDC minimizes the size of prediction sets while guaranteeing that the set-valued predictions contain the true label with a user-defined probability, building trust in decision-making. The superiority of CMDC is validated through comprehensive theoretical analysis and empirical experiments on various multi-view datasets.

Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant clinical implications across various medical applications. However, existing methods often exhibit limitations in achieving satisfactory topological performance, particularly in terms of preserving connectivity. To address this challenge, we propose a novel deep-learning approach, termed Deep Closing, inspired by the well-established classic closing operation. Deep Closing first leverages an AutoEncoder trained in the Masked Image Modeling (MIM) paradigm, enhanced with digital topology knowledge, to effectively learn the inherent shape prior of tubular structures and indicate potential disconnected regions. Subsequently, a Simple Components Erosion module is employed to generate topology-focused outcomes, which refines the preceding segmentation results, ensuring all the generated regions are topologically significant. To evaluate the efficacy of Deep Closing, we conduct comprehensive experiments on 4 datasets: DRIVE, CHASE_DB1, DCA1, and CREMI. The results demonstrate that our approach yields considerable improvements in topological performance compared with existing methods. Furthermore, Deep Closing exhibits the ability to generalize and transfer knowledge from external datasets, showcasing its robustness and adaptability. The code for this paper has been available at: https://github.com/5k5000/DeepClosing.

Multi-view deep classification expects to obtain better classification performance than using a single view. However, due to the uncertainty and inconsistency of data sources, adding data views does not necessarily lead to the performance improvements in multi-view classification. How to avoid worsening classification performance when adding views is crucial for multi-view deep learning but rarely studied. To tackle this limitation, in this paper, we reformulate the multi-view classification problem from the perspective of safe learning and thereby propose a Safe Multi-view Deep Classification (SMDC) method, which can guarantee that the classification performance does not deteriorate when fusing multiple views. In the SMDC method, we dynamically integrate multiple views and estimate the inherent uncertainties among multiple views with different root causes based on evidence theory. Through minimizing the uncertainties, SMDC promotes the evidences from data views for correct classification, and in the meantime excludes the incorrect evidences to produce the safe multi-view classification results. Furthermore, we theoretically prove that in the safe multi-view classification, adding data views will certainly not increase the empirical risk of classification. The experiments on various kinds of multi-view datasets validate that the proposed SMDC method can achieve precise and safe classification results.

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classification process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. Comparing with the traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. Specifically, we construct an evidence extractor at each classification layer to extract multilayer (multi-grained) evidence for image classification. To fuse the extracted multi-grained evidence from coarse to fine, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into fine-grained classes to enhance the classification performances. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of fine-grained classification. The comparison with state-of-the-art FGIC methods shows that our proposed method achieves competitive performances.

Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i.e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. It is required to reduce the uncertainty in data fusion and implement the trusted multi-view deep learning. Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multi-view deep learning method. Within this method, we adopt evidence theory to formulate the uncertainty of opinions as learning results from different data sources and measure the uncertainty of opinion aggregation as multi-view learning results through evidence accumulation. We prove that accumulating the evidences from multiple data views will decrease the uncertainty in multi-view deep learning and facilitate to achieve the trusted learning results. Experiments on various kinds of multi-view datasets verify the reliability and robustness of the proposed multi-view deep learning method.

As an unsupervised ensemble learning strategy, clustering ensemble combines multiple base clusterings into a high-quality one and has achieved successful applications in image analysis and data mining. However, extant clustering ensemble methods are ineffective to handle the data uncertainty in clustering consensus process, which may mislead to poor clustering ensemble results. To tackle the problem, we propose a stable clustering ensemble (SCE) method based on evidence theory (Dempster–Shafer theory) in this paper. Specifically, we construct a belief function of cluster membership to measure the uncertainty and stability of data instances in clustering ensemble and thereby implement the stable clustering ensemble algorithm. We test the proposed stable clustering ensemble method in the tasks of structural data clustering and image segmentation. The experimental results validate the proposed method is effective to process the uncertain data and produce high-quality data clusterings.