New AI Method Fuses Expert Opinions to Map Lung Vessels With Calibrated Confidence

Quantifying how blood vessels remodel in diseased lungs has long been one of the most tedious bottlenecks in pulmonary research. Pathologists studying vascular changes associated with pulmonary hypertension must trace and outline vessel after vessel under a microscope, converting stained tissue sections into precise digital measurements. The work is slow, expert-dependent and difficult to scale, yet the numbers it produces underpin how researchers judge disease severity and treatment response. A team at the University of Science, Ho Chi Minh City, working with Vietnam National University, has now introduced a machine learning framework designed to automate this labor without sacrificing the reliability that clinical quantification demands.

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