Evidence map›Paper›PMID 42806269›Full record

ArticleInterdisciplinary sciences, computational life sciences2026

3D UXFormer: Dual-Branch Feature Fusion for Precise Breast MRI Multi-Tissue Segmentation.

Xiaodong Zheng, Zhitao Wei, Siyao Du, Chinting Wong, Yanting Liang, Yanfen Cui, Jinrong Qu, Jianlin Wu, Lina Zhang, Chu Han and 5 more

Abstract read
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In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Xiaodong Zheng *Institution of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
Zhitao Wei *Institution of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
Siyao Du *Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Chinting Wong *Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Yanting LiangDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Yanfen CuiGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
Jinrong QuDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
Jianlin WuDepartment of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
Lina ZhangDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Chu HanDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Zaiyi LiuDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Ying WangDepartment of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China. liuivy527@163.com.
Peng XuInstitution of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China. gdxupeng@gzhu.edu.cn.
Zhenwei ShiDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China. shizhenwei@gdph.org.cn.
Wenbin LiuInstitution of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China. wbliu6910@gdhu.edu.cn.ORCID https://orcid.org/0000-0001-9091-3177

Funding

Applied Basic Research Projects of Shanxi Province, China, Outstanding Youth Foundation 202103021222014Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application 2022B1212010011Key-Area Research and Development Program of Guangdong Province, China 2021B0101420006Municipal School Joint Fund of Guangzhou Science and Technology Bureau SL2022A03J00935National Natural Science Foundation of China 62002079National Natural Science Foundation of China 62072128National Natural Science Foundation of China 62573143National Natural Science Foundation of China 82472062National Science Foundation for Young Scientists of China 82001789National Science Foundation for Young Scientists of China 82302165National Science Foundation for Young Scientists of China 82371947National Science Foundation for Young Scientists of China for Young Scientists of Shanxi Provinc 62102103Natural Science Foundation of Guangdong Province 2023A1515011401Natural Science Foundation of Guangdong Province 2024A1515011672Regional Innovation and Development Joint Fund of National Natural Science Foundation of China U22A20345Science Fund for Distinguished Young Scholars of Guangdong Province 2023B1515020043
6 · The paper itself

Abstract

Accurate segmentation of breast tissues in MRI is essential for precise breast cancer diagnosis and effective treatment planning. Manually segmenting these tissues is not only arduous but also time-consuming. Fusing multi-phase MRI images, like the Contrast-enhanced Early ([Formula: see text]) and Contrast-enhanced Peak ([Formula: see text]) phases, to fully utilize their complementary information remains a challenge. In this paper, we introduce a novel 3D UXFormer model. It combines the 3D UNET and Vision Transformer (ViT) through a dual-branch structure, capitalizing on the 3D UNET's local feature extraction prowess and ViT's global feature-capturing ability. To address data-related issues, we pre-train ViT using the Masked Autoencoder (MAE) approach. Extensive experiments demonstrate that the 3D UXFormer outperforms state-of-the-art algorithms, achieving higher Dice and IoU scores across diverse breast tissues in different datasets. The source code of our method is available on Github.

Indexed as

Convolutional neural networksDCE-MRIMulti-tissue segmentationVision transforme

Identifiers

PMID42806269

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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.