ArticleScientific reports2026
SlideMamba: entropy-based adaptive fusion of GNN and Mamba for enhanced representation learning in digital pathology.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- AI-based digital pathology and spatial proteomics enable precision oncology: A case report of recurrent melanoma in a young patient.NPJ precision oncology · 2026Article
- A hybrid ST-ViT-driven multimodal architecture combining spatiotemporal MRI patterns and radiomic features for enhanced prediction of pCR in neoadjuvant breast cancer therapy.Biology direct · 2026Article
- A patient-aware benchmarking of CNN and transformer architectures for breast cancer histopathology classification.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole-slide image (WSI) analysis requires integrating fine-grained spatial structure with long-range tissue context. This work introduces SlideMamba, a hybrid framework that performs embedding-level fusion of a graph neural network (capturing local topology) and a Mamba state-space branch (modeling global context) via entropy-based confidence weighting. The adaptive fusion emphasizes the branch with lower predictive entropy, providing a principled mechanism to combine complementary feature streams and improving multi-scale representation learning. Effectiveness is demonstrated on two clinically relevant tasks with class imbalance: (i) mutation/fusion prediction from the OAK clinical trial WSIs (40×), where SlideMamba attains PRAUC [Formula: see text], exceeding fixed-fusion (GAT-Mamba [Formula: see text]) and single-branch baselines (Mamba [Formula: see text], SlideGraph+ [Formula: see text], MIL [Formula: see text], TransMIL [Formula: see text]); and (ii) LUAD vs. LUSC classification on an independent proprietary cohort (20×), where SlideMamba achieves PRAUC of [Formula: see text], outperforming MIL (0.946 ± 0.037), TransMIL (0.929 ± 0.033), SlideGraph+ (0.945 ± 0.025), GAT-Mamba (0.935 ± 0.011), Mamba (0.962 ± 0.012). Beyond performance gains, the inclusion of the Mamba backbone ensures computational efficiency by avoiding the quadratic complexity of standard attention mechanisms. Furthermore, the adaptive fusion weights provide inherent interpretability, offering clinicians insight into whether local cellular graphs or global tissue architecture drove the final prediction. These attributes suggest SlideMamba offers a clinically feasible path toward spatially-resolved, precision computational pathology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.