Asymmetric encoder-decoder U-net with hybrid attention and residual dilated convolution for brain tumor segmentation

Overview

1. The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
2. College of Information Engineering, Henan University of Science and Technology, Luoyang, China

Abstract

Inroduction: The accurate and automatic segmentation of brain tumors from magnetic resonance imaging is regarded as essential for clinical diagnosis and surgical planning. Nevertheless, the concurrent extraction of both local fine details and global contextual cues remains challenging, primarily due to ambiguous tumor margins and marked disparities in tumor dimensions.

Methods: To address these issues, a novel HCSA-RMSDC U-Net is designed in this work, which is characterized by an asymmetric architecture with a computationally heavy encoder and a lightweight decoder. Specifically, a hybrid channel–spatial attention (HCSA) module is incorporated into the encoder, wherein axial decomposed convolution is utilized to capture directional spatial information, thereby enhancing spatial feature representation for tumor segmentation. Concurrently, a residual multi-scale dilated convolution (RMSDC) module is deployed in the bottleneck layer to capture semantic information across varying scales without increasing the spatial resolution.

Results: The proposed model was evaluated on the publicly available BRISC 2025 and Figshare datasets, achieving Dice coefficients of 87.39% and 82.84%, respectively

Discussion: These findings support the complementary roles of the HCSA and RMSDC mechanisms in enhancing feature representation and multi-scale contextual modeling. Overall, the proposed framework provides a fully automated approach for brain tumor segmentation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data and to no code of its authors: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• figshare:1512427, at figshare; found in “Data availability statement”
• kaggle.com/datasets/briscdataset, at Kaggle; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://www.kaggle.com/datasets/briscdataset/brisc2025 and https://figshare.com/articles/dataset/brain_tumor_dataset/1512427.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 12 November 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 52 references.

Cite

This paper

Cao, X., Song, X., Zhang, J., Chen, W., & Du, G. (2026). Asymmetric encoder-decoder U-net with hybrid attention and residual dilated convolution for brain tumor segmentation. Frontiers in neuroscience, 20, 1999080.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41597-026-06753-y

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification.

Journal: Scientific data

In common: kaggle.com/datasets/briscdataset, figshare 1512427, structural MRI / diffusion, other condition, 1 reference

[2] doi:10.3390/brainsci16050468

Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.

Journal: Brain sciences

In common: figshare 1512427, structural MRI / diffusion, other condition, 3 references

[3] doi:10.1038/s41598-025-09833-y [code]

A novel hybrid vision UNet architecture for brain tumor segmentation and classification.

Journal: Scientific reports

In common: figshare 1512427, other condition, 3 references

[4] doi:10.1038/s41598-025-23820-3

A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features.

Journal: Scientific reports

In common: kaggle.com/datasets/briscdataset, structural MRI / diffusion, other condition, 1 reference

[5] doi:10.3390/brainsci16010037

A Multi-Task EfficientNetV2S Approach with Hierarchical Hybrid Attention for MRI Enhancing Brain Tumor Segmentation and Classification.

Journal: Brain sciences

In common: structural MRI / diffusion, other condition, 5 references

[6] doi:10.3390/jcm15145508

Region-Wise Bézier Intensity Augmentation for Domain-Generalized Brain Tumor Segmentation with a Mamba U-Net.

Journal: Journal of clinical medicine

In common: figshare 1512427, structural MRI / diffusion, other condition, 2 references

[7] doi:10.3390/diagnostics16172806

Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.

Journal: Diagnostics (Basel, Switzerland)

In common: structural MRI / diffusion, other condition, 5 references

[8] doi:10.1038/s41598-023-45456-x [code]

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Journal: Scientific reports

In common: structural MRI / diffusion, other condition, 5 references

[9] doi:10.1038/s41598-025-11574-x

Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches.

Journal: Scientific reports

In common: figshare 1512427, structural MRI / diffusion, other condition, 2 references

[10] doi:10.3389/fgene.2026.1814786

Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.

Journal: Frontiers in genetics

In common: figshare 1512427, structural MRI / diffusion, other condition, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.