App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Disentangled and stain-normalized co-attention-based fusion network

Zhang Huinan1a
Qiang Yan2
Cai Meiling1a
Zhao Juanjuan1a,1b
Zhang Runqi1a
Wang Long3
Yang Qianqian3
1. a. College of Computer Science & Technology(College of Data Science), b. School of software, Taiyuan University of Technology, Jinzhong Shanxi 030600, China
2. School of Software, North University of China, Taiyuan 030051, China
3. School of Data Science & Information Engineering, Jinzhong College of Information, Jinzhong 030600, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0367

Abstract

Non-small cell lung cancer includes lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) , with subtypes showing significant differences in molecular mechanisms and treatment responses. Pathological images and genomic data provide complementary information. However, images are affected by staining variations, reducing the stability of structural representations. Cross-modal data exhibit heterogeneity and nonlinear relationships, complicating interaction modeling. To address these issues, the study proposed a disentangled stain-normalized co-attention-based multimodal fusion network (DISCOFusion) . The network used an encoder–decoder structure with octave convolution to decompose high and low frequency features for enhanced representation and applied content-style feature alignment to achieve stain normalization while preserving key tissue structures. The network actively fuses normalized images and genomic features across modalities, captures deep interactions in a shared latent space, and preserves modality-specific information to enhance NSCLC subtype classification. Experiments on the TCGA-NSCLC dataset show an AUC of 94.15%, outperforming existing methods.

Foundation Support

国家自然科学基金资助项目(62376183)
中央引导地方科技发展基金资助项目(YDZJSX2022C004)
国家自然科学基金合作项目(U21A20469)
山西省科技创新人才团队专项资助项目(202304051001009)
国家自然科学基金委员会项目(62476190)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0367
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-07] Accepted Paper

Cite This Article

张慧楠, 强彦, 蔡美龄, 等. 用于肺癌亚型分类的解耦染色归一化共注意力多模态融合网络 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0367. (Zhang Huinan, Qiang Yan, Cai Meiling, et al. Disentangled and stain-normalized co-attention-based fusion network [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0367. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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