The core insight behind UbiQVision is that explainable artificial intelligence, or XAI, and uncertainty quantification have usually been treated as separate problems, when in fact they are inseparable in high-stakes medicine. The dominant explanation technique for medical imaging is SHAP, short for SHapley Additive exPlanations, a game-theoretic method that assigns each pixel a contribution score indicating how much it pushed the model toward or away from a diagnosis. SHAP produces visually compelling heatmaps that clinicians find intuitive. But the method carries hidden assumptions. Standard SHAP formulations effectively treat features as independent, while pixels in medical images are strongly correlated. When the underlying data distribution is misspecified or estimated from small, biased samples, SHAP values can become unstable, producing misleading rankings of imaging biomarkers or spurious emphasis on artifacts. Clinicians, susceptible to automation bias, may over-trust visually appealing heatmaps that do not faithfully reflect the model’s true reasoning.

UbiQVision addresses this by unifying three mathematical disciplines into a single pipeline. First, the researchers constructed a heterogeneous ensemble of three distinct neural network architectures: a lightweight custom convolutional neural network, the widely used residual network ResNet-18, and a Vision Transformer pre-trained on ImageNet. Architectural diversity matters because it ensures the models’ errors are not perfectly correlated, a prerequisite for meaningful evidence fusion. Second, instead of averaging the ensemble’s predictions uniformly, the framework applies Bayesian meta-learning. Each model’s reliability is modeled as a random variable following a Dirichlet distribution, updated with validation performance scores such as F1 metrics. A temperature parameter controls how sharply the weighting favors the strongest model, and sampling from this posterior gives each model a probabilistic vote that rewards robust performers while preserving the influence of weaker models that may have learned strong local evidence.

The third and most novel component is the transformation of SHAP attributions into basic probability assignments within Dempster–Shafer evidence theory, a classical framework for reasoning under uncertainty. Using a hyperbolic tangent transformation scaled by a sensitivity parameter, the framework maps unbounded, real-valued SHAP scores into bounded evidential masses. Positive attributions become mass supporting the target diagnosis, negative attributions become mass supporting its negation, and any leftover mass is assigned to the universal set, representing total epistemic ignorance. Dempster’s rule of combination then fuses the weighted masses from all three models into pixel-level maps of belief, plausibility, and uncertainty. A conflict coefficient, computed during fusion, explicitly quantifies where the models disagree, rather than smoothing that disagreement away as conventional ensemble averaging does.

The resulting outputs map directly onto clinical concepts. The belief map marks regions where the ensemble has reached confirmed consensus, such as the dark, ring-like chromatin structures of a malaria parasite inside an infected red blood cell. The plausibility map captures the upper bound of what could be true, exposing internal conflict when, for example, the noisy ResNet model highlights random tissue as pathological while the other models disagree. The uncertainty map quantifies total ignorance: bright yellow regions signal that the model genuinely knows nothing, correctly covering empty slide background or out-of-distribution inputs, while dark purple regions indicate the model has sufficient evidence to decide. This explicit separation of confirmed disease, conflicting opinions, and insufficient data is precisely what standard softmax classifiers, which force every pixel into a category, cannot provide.

The team evaluated the framework across three publicly available medical imaging datasets spanning histology, neuroimaging, and ophthalmology. On the NIH malaria dataset of 27,558 balanced blood smear images, the Bayesian weighting identified the custom CNN as the primary expert with a posterior weight of roughly 0.37, and the fused belief maps performed what amounts to semantic segmentation of the parasite, filtering out the cell wall and cytoplasm as irrelevant background. Ten-fold stratified cross-validation showed highly consistent macro F1 scores: ResNet averaged 96.2 percent, with the custom CNN and Vision Transformer close behind at 95.7 percent. Local Lipschitz stability analysis confirmed that the SHAP attributions feeding the fusion were mathematically stable, with all three architectures scoring below 0.0012, indicating the maps reflect genuine features rather than unstable gradient noise.

The Alzheimer’s disease experiments revealed perhaps the most clinically resonant behavior. Using T1-weighted MRI scans graded across four dementia stages, the framework captured the non-linear progression of brain atrophy by modulating its evidential confidence with disease severity. In moderate dementia cases, positive attributions aligned precisely with enlarged ventricular boundaries, and the belief map showed dense, localized clusters of confirmed pathological evidence. For very mild dementia, where atrophy is subtle and easily confused with healthy aging, the uncertainty maps showed widespread high entropy, mirroring the genuine diagnostic difficulty that human radiologists face. Notably, the framework exposed a well-known weakness in the field: the very mild dementia class produced the highest mean fused uncertainty, correctly signaling that the ensemble was operating near the limits of its knowledge rather than masking that limitation behind a confident label.

On the diabetic retinopathy dataset from the EyePACS Kaggle competition, the framework faced its hardest test, a five-class ordinal grading problem with subtle transitions between severity levels. Here the custom CNN struggled, achieving a mean macro F1 of only 46.1 percent, while the Vision Transformer and ResNet reached 68.7 and 67.9 percent respectively. The framework adapted, and its uncertainty behavior tracked clinical reality: severe diabetic retinopathy, characterized by massive hemorrhages and extensive ischemia, elicited the lowest median uncertainty, while proliferative disease with its ambiguous, newly forming vascular anomalies produced the highest. Ablation studies across all three datasets confirmed that progressive Gaussian blur, which destroys anatomical structure, caused mean fused uncertainty to rise monotonically, demonstrating that the framework’s ignorance estimates genuinely track epistemic uncertainty arising from missing structural information.

Beyond the maps themselves, selective prediction risk-coverage analysis showed that UbiQVision provides superior uncertainty calibration compared with deep ensemble variance, Monte Carlo dropout, and integrated gradients baselines. On the malaria dataset, the framework maintained a residual error rate of zero up to roughly 35 percent coverage, while baseline methods exhibited dangerous overconfidence spikes at lower coverage levels. The framework is entirely post-hoc and model-agnostic at the ensemble level, requiring no modification to validated training pipelines, which distinguishes it from evidential deep learning approaches that demand specialized loss functions. The authors acknowledge real limitations: computational cost is substantial, with inference times of 0.55 to 1.03 seconds per image and peak memory demands of 7.5 to 7.7 gigabytes, and image resolution was constrained to 128 by 128 pixels for most models due to the memory requirements of pixel-wise SHAP computation. Shared blind spots among models trained on identical data could also undermine the uncertainty estimates under adversarial conditions.

Even so, the implications for safety-critical medical AI are considerable. By making the unknown unknowns visible, the framework allows clinical workflows to route high-confidence predictions for expedited validation while directing uncertain or contested cases to expert review, a distinction directly relevant to regulatory requirements under the EU AI Act, which mandates transparency, robustness, and explainability in high-risk medical systems. The researchers envision extending the evidential fusion to multi-modal and longitudinal settings, tracking belief and ignorance at the patient level over time, and using the uncertainty outputs to drive active learning. The code is publicly available on GitHub, and the framework’s deeper contribution may be conceptual: it reframes medical AI from a binary classifier that masquerades confidence as certainty into a risk assessment tool that communicates, pixel by pixel, exactly how much it knows, how much it doubts, and where it is simply guessing.

Subject of Research: Uncertainty-aware explainable AI framework for reliable deep learning ensembles in medical imaging

Article Title: UbiQVision: Spatial Dempster-Shafer fusion of XAI attributions for reliable deep vision ensembles

Article References: Dubey, A., Anžel, A., İlgen, B., & Hattab, G. (2026). UbiQVision: Spatial Dempster–Shafer fusion of XAI attributions for reliable deep vision ensembles. Machine Learning with Applications, 25, Article 101000. https://doi.org/10.1016/j.mlwa.2026.101000

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.101000

Keywords: explainable AI, uncertainty quantification, Dempster–Shafer theory, medical imaging, deep learning ensembles, SHAP, Bayesian meta-learning, malaria detection, Alzheimer's disease, diabetic retinopathy, Vision Transformers, clinical decision support

The new method, called ReliFuse, is described in the journal Machine Learning and addresses a familiar irony in modern medical image analysis. Deep neural networks have become remarkably good at segmenting anatomical structures from histological images, producing masks that can rival human annotations. However, no single network is perfect, and the errors that individual models make are often complementary: one expert model may miss a faint peripheral vessel that another catches, while the second mislabels a fold of tissue that the first correctly ignores. Rather than treating these disagreements as noise, the Vietnamese team treats them as information, formulating the segmentation task as a problem of posterior fusion, in which multiple frozen expert models pool their predictions into a single, better-calibrated output.

What distinguishes ReliFuse from many ensemble techniques is a striking design constraint. At the fusion stage, the framework never looks at the underlying color image at all. Instead, it operates purely on cached probability maps produced beforehand by a bank of seven independently trained segmentation experts. These probability maps encode, for every pixel, how strongly each expert believes that the pixel belongs to a vessel. The fusion head then constructs so-called ensemble-state features from this stack of opinions, describing where the experts agree, where they diverge, and how their confidence is distributed. Working in logit space rather than raw probabilities, the method pools the evidence from all experts, estimates how trustworthy each local expert opinion is, and applies corrections only where the ambiguity is genuinely high.

Reliability estimation is the conceptual heart of the framework. For each expert model, the researchers compute validation-anchored priors from the model’s behavior on held-out validation data, giving the fusion head a sense of each expert’s typical strengths and weaknesses before it ever sees a test case. These priors are combined into a calibrated consensus opinion that serves as the starting point for the final segmentation. Crucially, ReliFuse does not rewrite the whole map. Its residual correction branch is bounded and gated by an ambiguity field, so that confident agreement among experts is preserved unchanged while corrections are concentrated exclusively in the contested regions where experts disagree or where boundary transitions are uncertain. This consensus-preservation principle ensures that the fusion step can refine the output without corrupting regions where the ensemble is already correct.

Training the fusion head is itself a multi-objective undertaking. The researchers combine an overlap loss with a boundary loss, a calibration loss, a consensus-preservation loss and a sparse-correction penalty. The boundary term compares gradient magnitudes between the predicted mask and the annotation, making contour errors visible even when vessels occupy few pixels. The consensus term is deliberately asymmetric, using stop-gradient operators to prevent the model from pulling its prior toward its own output or from simply lowering the ambiguity gate to dodge penalties. The sparse penalty is applied to the gated correction actually added to the logits, discouraging the network from making dense modifications everywhere rather than surgical fixes in ambiguous places. The calibration term supervises both the pooled prior and the final posterior with a Brier-style error, keeping the system’s confidence honest.

On a publicly available dataset of rat lung histology images with expert-annotated vessel masks, ReliFuse achieved the highest primary overlap among all methods in a matched comparison that gave every learned fusion head the same seven-expert posterior stack. The gains over the strongest competing learned fusion heads, which include ensemble-from-multiple-annotations approaches such as D-LEMA and locally calibrated federated methods such as LC-Fed, are modest in raw Dice and IoU terms. The authors are candid about this. In a paired statistical analysis across the held-out batches, the differences against these strongest references were small and not statistically significant, and the team treats those rows as evidence about effect direction and magnitude rather than proof of broad superiority.

Where ReliFuse genuinely pulls ahead is in the conditions that stress fusion methods hardest. In stress tests isolating batches with high expert disagreement and high vessel content, the improvements were clearest, consistent with the framework’s design focus on ambiguity and minority evidence. The method also held its own on boundary quality: while P-MoLE recorded the best boundary F1 scores and D-LEMA led on distance metrics such as HD95, ReliFuse remained close on these contour measures, indicating that its overlap gains did not come at the cost of degraded vessel geometry. A calibration and morphology analysis showed no single method dominating every diagnostic, with LC-Fed best on calibration error and D-LEMA best on centerline overlap, but ReliFuse remained competitive across morphology measures while producing the strongest primary Dice and IoU in the matched benchmark.

The practical economics of the approach are part of its appeal. Because the experts run only once and their probability maps are cached, the fusion stage is dramatically cheaper than re-running full segmentation networks. In the researchers’ profiling experiments, recomputing the seven raw-image experts required roughly 9,357 milliseconds per batch of four images and more than 13.4 gigabytes of peak memory, far exceeding the cost of any cached-fusion pass. ReliFuse is slower than naive averaging because it must construct its diagnostic state, estimate calibrated opinions and apply gated corrections, but its parameter count remains modest relative to the base experts, and the framework is designed for settings where multiple frozen models are already available from prior development work.

The study is also notable for its methodological transparency. The authors report a full sensitivity analysis of how the expert bank is constructed, showing that a diversity-aware selection of experts improved every matched fusion rule compared with simply choosing the seven highest-scoring models. Ablation studies confirmed that the ambiguity gate, calibration supervision, boundary emphasis and consensus-preservation terms each contribute to the framework’s behavior, and the team documents a failure case in which the same gate that recovers a faint vessel can enlarge a false-positive region when the posterior evidence is misleading. By reporting the complete hard-subset stress matrices and labeling exploratory statistics as such, the researchers offer a template for honest evaluation in the crowded field of medical image segmentation.

For the researchers who need these measurements, the implications are concrete. The dataset underlying the work, published by Sinitca and colleagues in Scientific Data in 2024, contains 609 paired microphotographs and binary masks from rat models of pulmonary hypertension, split here into 517 development images and 92 held-out test images. The ReliFuse source code is publicly available on GitHub, and the framework requires no retraining of the underlying expert networks, only the lightweight fusion head. As quantitative histology moves from hand-tracing toward automated pipelines, ReliFuse suggests a pragmatic middle path: rather than chasing ever-larger single models, laboratories can combine the complementary strengths of the models they already have, and let a calibrated arbiter decide, pixel by pixel, whose opinion to trust.

The intellectual lineage of this approach stretches back several decades. Stacked generalization, introduced by Wolpert in 1992, established the idea of training a secondary learner to combine the outputs of base models, and Dietterich’s foundational work on ensemble methods later explained why combining diverse classifiers so often outperforms any single member. ReliFuse adapts this classical principle to dense prediction, where every pixel rather than every sample must receive a fused verdict, and where the cost of naively rerunning large networks makes caching an attractive design choice.

The framework also draws on a well-developed literature concerning the overconfidence of modern neural networks. Guo and colleagues demonstrated in 2017 that contemporary deep classifiers frequently produce probabilities that are poorly calibrated with respect to true correctness, a concern that is especially acute in medical settings where downstream decisions may hinge on a confidence value. Related work by Lakshminarayanan and colleagues on deep ensembles showed that simply averaging independently trained networks yields surprisingly strong uncertainty estimates, which helps explain why the reliability priors anchored on validation behavior prove so informative in the fusion stage.

Vessel segmentation itself has a long algorithmic history predating deep learning. Multiscale vessel-enhancement filtering, pioneered by Frangi and colleagues in 1998, remains influential, and subsequent surveys catalogued the breadth of methods, datasets and evaluation metrics used to judge tubular structure extraction. The pulmonary histology setting adds distinctive challenges, including stained tissue texture, irregular vessel branching and a pronounced class imbalance favoring background pixels, which is why overlap measures such as Dice and IoU are typically complemented by boundary and centerline diagnostics in this domain.

By situating posterior fusion within these established traditions, the study connects classical ensemble theory, calibration research and vascular image analysis into a single practical pipeline for quantitative histopathology.

Subject of Research: A reliability-calibrated machine learning framework that fuses multiple segmentation experts' probability maps to automate histological pulmonary vessel segmentation.

Article Title: ReliFuse: Reliability-Calibrated Posterior Fusion for Histological Vessel Segmentation

Article References: Le, T. P., Nguyen, T. N., Tran, V. L. H., Doan, T. T., Nguyen, B. T., & Huynh, S. T. (2026). ReliFuse: Reliability-Calibrated Posterior Fusion for Histological Vessel Segmentation. Machine Learning, 115(9), Article 218. https://doi.org/10.1007/s10994-026-07154-3

Image Credits: AI Generated

DOI: 10.1007/s10994-026-07154-3

Keywords: machine learning, ReliFuse, histological vessel segmentation, posterior fusion, pulmonary hypertension, medical image analysis, deep learning ensembles, uncertainty calibration, ensemble segmentation, computational pathology, vessel remodeling, segmentation