Evidence map›Paper›PMID 40866450›Full record

ArticleScientific reports2025

Quantum integration in swin transformer mitigates overfitting in breast cancer screening.

Zongyu Xie, Xiaoguang Yang, Shuni Zhang, Jingru Yang, Yun Zhu, Aoqi Zhang, Haitao Sun, Qun Dai, Lei Li, Hongde Liu and 2 more

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

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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

12 authors.

Zongyu Xie *Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Xiaoguang Yang *Origin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China.
Shuni ZhangDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Jingru YangDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Yun ZhuDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Aoqi ZhangDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
Haitao SunDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Qun DaiOrigin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China.
Lei LiOrigin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China.
Hongde LiuState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
Wenlong MingJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Menghan DouOrigin Quantum Computing Technology (Hefei) Co., Ltd., Hefei, 230088, China. dmh@originqc.com.

Funding

Anhui Province Medical Imaging Discipline (Specialty) Leader Cultivation Program DTR2024028Anhui Provincial Science and Technology Innovation Major Project (Key Research and Development Program) 202423s06050001Bengbu Medical University Special Program for Key Support and Cultivation Discipline Areas (Quantum Medicine) 2024bypy017Clinical research project, Bengbu Medical University 2022byflc008Key Research and Development Projects of Anhui Province 2022e07020033National Key Research and Development Program of China - Young Scientists Project 2023YFB4502500
6 · The paper itself

Abstract

To explore the potential of quantum computing in advancing transformer-based deep learning models for breast cancer screening, this study introduces the Quantum-Enhanced Swin Transformer (QEST). This model integrates a Variational Quantum Circuit (VQC) to replace the fully connected layer responsible for classification in the Swin Transformer architecture. In simulations, QEST exhibited competitive accuracy and generalization performance compared to the original Swin Transformer, while also demonstrating an effect in mitigating overfitting. Specifically, in 16-qubit simulations, the VQC reduced the parameter count by 62.5% compared with the replaced fully connected layer and improved the Balanced Accuracy (BACC) by 3.62% in external validation. Furthermore, validation experiments conducted on an actual quantum computer have corroborated the effectiveness of QEST.

Indexed as

Breast NeoplasmsDeep LearningEarly Detection of CancerQuantum TheoryComputer SimulationFemaleHumansArtificial intelligenceBreast cancerDigital mammographyQuantum computing

Identifiers

PMID40866450
PMCPMC12391448

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.