Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering_Journal of Biomedical Engineering

Authors：

SU Shuzhi ^1,2,3 , ZHANG Kexue ³ , ZHU Yanmin ^2,4 , ZHONG Xiaoni ⁵ , XIANG Liu ³ ,  DAI Yong ^1,6

1. The First Hospital, Anhui University of Science & Technology, Huainan, Anhui 232001, P. R. China;
2. Joint Research Center for Occupational Medicine and Health of IHM, Anhui University of Science & Technology, Huainan, Anhui 232001, P. R. China;
3. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, Anhui 232001, P. R. China;
4. School of Mechatronics Engineering, Anhui University of Science & Technology, Huainan, Anhui 232001, P. R. China;
5. Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University/The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong 518020, P. R. China;
6. School of Medicine, Anhui University of Science & Technology, Huainan, Anhui 232001, P. R. China;

Corresponding?author：

DAI Yong, Email: daiyong22@aust.edu.cn

Keywords：

Colorectal cancer; Histopathology; Dynamic gland-aware; Tissue soft-clustering

DOI：

10.7507/1001-5515.202603029

Video：

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Abstract Full text Figures/Tables Video References Cited by

In colorectal cancer histopathology, the collaborative perception of microscopic and salient lesions is critical for effective diagnosis and improved patient prognosis. However, existing deep learning methods struggle to simultaneously capture microscopic glandular disorganization and salient tissue lesions. To address this limitation, a colorectal cancer diagnosis network based on dynamic gland-aware and tissue soft-clustering (DGTSNet) is proposed. The method employs dynamic gland-aware convolution to explicitly perceive gland boundaries and dynamically adjust sampling offsets, while incorporating a continuous-domain constraint to prevent out-of-bound sampling, thereby enhancing the perception of microscopic glandular disorders. Meanwhile, a tissue soft-clustering module is utilized to adaptively generate clustering prototypes and guide pixels toward relevant prototype centroids according to semantic similarity, suppressing irrelevant background interference and enhancing responses to significant tissue lesions. Finally, a cascaded sparse coupling module is introduced to collaboratively modulate cross-semantic feature representations along both the spatial and channel dimensions, while constructing differentiable masks to suppress low-contribution semantics, thereby achieving collaborative coupling of cross-semantic features. Experimental results demonstrated that the proposed method achieved superior performance on the Chaoyang, Kather-5K, and EBHI datasets, as well as a real-world clinical validation cohort, outperforming multiple baseline models. The study shows that the proposed method can effectively enhance the joint perception of microscopic glandular disorders and significant tissue lesions, providing an effective solution for colorectal cancer diagnosis.

Citation： SU Shuzhi, ZHANG Kexue, ZHU Yanmin, ZHONG Xiaoni, XIANG Liu, DAI Yong. Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering. Journal of Biomedical Engineering, 2026, 43(3): 450-460. doi: 10.7507/1001-5515.202603029 Copy

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35.	Wang Ao, Chen Hui, Lin Zijia, et al. LSNet: see large, focus small//2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2025: 9718-9729.
36.	Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale//9th International Conference on Learning Representations (ICLR). Virtual Event: OpenReview.net, 2021.
37.	Liu Ze, Lin Yutong, Cao Yue, et al. Swin transformer: hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021: 9992-10002.
38.	Sandler M, Howard A, Zhu Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 4510-4520.
39.	Yue Yubiao, Li Zhenzhang. MedMamba: vision Mamba for medical image classification. arXiv, 2024: 2403.03849(v5).
40.	Huo Xiangzuo, Sun Gang, Tian Shengwei, et al. HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed Signal Process Control, 2024, 87: 105534.

1. Meng Yang, Tan Zongbiao, Zhen Junhai, et al. Global, regional, and national burden of early-onset colorectal cancer from 1990 to 2021: a systematic analysis based on the Global Burden of Disease Study 2021. BMC Med, 2025, 23(1): 34.
2. Zhou Jiajie, Yang Qizhi, Zhao Shuai, et al. Evolving landscape of colorectal cancer: global and regional burden, risk factor dynamics, and future scenarios (the Global Burden of Disease 1990–2050). Ageing Res Rev, 2025, 104: 102666.
3. Carbone F, Spinelli A, Ciardiello D, et al. Prognosis of early-onset versus late-onset sporadic colorectal cancer: systematic review and meta-analysis. Eur J Cancer, 2025, 215: 115172.
4. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
5. Mattiuzzi C, Sanchis-Gomar F, Lippi G. Concise update on colorectal cancer epidemiology. Ann Transl Med, 2019, 7(21): 609.
6. Siegel R L, Wagle N S, Cercek A, et al. Colorectal cancer statistics, 2023. CA Cancer J Clin, 2023, 73(3): 233-254.
7. Pacal I, Attallah O. Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction. Knowl Based Syst, 2025, 319: 113625.
8. Van Toledo D E F W M, IJspeert J E G, Bossuyt P M M, et al. Serrated polyp detection and risk of interval post-colonoscopy colorectal cancer: a population-based study. Lancet Gastroenterol Hepatol, 2022, 7(8): 747-754.
9. Ke Q, Hum Y C, Yap W S, et al. Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion. Sci Rep, 2025, 15(1): 35155.
10. Junaid H H S, Daneshfar F, Mohammad M A. Automatic colorectal cancer detection using machine learning and deep learning based on feature selection in histopathological images. Biomed Signal Process Control, 2025, 107: 107866.
11. De Gordoa K S, Daca-Alvarez M, Rodrigo-Calvo M, et al. Interobserver variability of histopathological assessment in pT1 colorectal carcinoma. Histopathology, 2026, 88(4): 868-880.
12. Verghese G, Lennerz J K, Ruta D, et al. Computational pathology in cancer diagnosis, prognosis, and prediction-present day and prospects. J Pathol, 2023, 260(5): 551-563.
13. 葛潮洋, 高亦凡, 劉成, 等. CGP-Net: 用于胃癌精準分割的跨模態引導先驗網絡. 生物醫學工程學雜志, 2026, 43(1): 146-153.
14. Addo D, Zhou S, Sarpong K, et al. A hybrid lightweight breast cancer classification framework using the histopathological images. Biocybern Biomed Eng, 2024, 44(1): 31-54.
15. Baumann E, Carre?o-Martínez J F, Frei A L, et al. Aligning computational pathology with clinical practice for colorectal cancer. NPJ Precis Oncol, 2025, 9(1): 381.
16. Luo C, Wang Y, Deng Z, et al. Colonic polyp segmentation based on transformer-convolutional neural networks fusion. Pattern Recognit, 2026, 170: 112116.
17. Pan Xingliang, Hua Bo, Tong Ke, et al. EL-CNN: an enhanced lightweight classification method for colorectal cancer histopathological images. Biomed Signal Process Control, 2025, 100: 106933.
18. Kumar A, Vishwakarma A, Bajaj V. CRCCN-Net: automated framework for classification of colorectal tissue using histopathological images. Biomed Signal Process Control, 2023, 79: 104172.
19. Azad R, Kazerouni A, Heidari M, et al. Advances in medical image analysis with vision transformers: a comprehensive review. Med Image Anal, 2024, 91: 103000.
20. 胡倫瑜, 夏威, 李瓊, 等. 基于自監督預訓練和多任務學習的肺腺癌無復發生存期預測. 生物醫學工程學雜志, 2024, 41(2): 205-212.
21. Willemink M J, Roth H R, Sandfort V. Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiol Artif Intell, 2022, 4(6): e210284.
22. Xu H, Xu Q, Cong F, et al. Vision transformers for computational histopathology. IEEE Rev Biomed Eng, 2023, 17: 63-79.
23. Zeid M A E, El-Bahnasy K, Abo-Youssef S E. Multiclass colorectal cancer histology images classification using vision transformers//2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS). Cairo: Ain Shams University, 2021: 224-230.
24. Li M. Transformer-based self-supervised learning and distillation for medical image classification: improving colorectal cancer detection on NCT-CRC-HE-100K with Swin-T V2//2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). Hangzhou: Zhejiang University of Water Resources and Electric Power, 2024: 644-648.
25. Takahashi S, Sakaguchi Y, Kouno N, et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J Med Syst, 2024, 48(1): 84.
26. De Oliveira D L L, Tosta T A A, Neves L A, et al. Hybrid CNN-transformer models in histopathology image analysis: a scoping review. IEEE Access, 2025, 13: 212887-212919.
27. Sathyanarayana B, Alampally S, Akella R, et al. ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection. J Cancer Res Clin Oncol, 2025, 151(7): 209.
28. Tanveer M, Akram M U, Khan A M. TransNetV: an optimized hybrid model for enhanced colorectal cancer image classification. Biomed Signal Process Control, 2024, 96: 106579.
29. Zhu Chuang, Chen Wenkai, Peng Ting, et al. Hard sample aware noise robust learning for histopathology image classification. IEEE Trans Med Imaging, 2022, 41(4): 881-894.
30. Liu Shiwei, Wang Liejun, Yue Wenwen. An efficient medical image classification network based on multi-branch CNN, token grouping transformer and mixer MLP. Appl Soft Comput, 2024, 153: 111323.
31. Ather J N, Weis C A, Bianconi F, et al. Multi-class texture analysis in colorectal cancer histology. Sci Rep, 2016, 6(1): 27988.
32. Hu W, Li C, Rahaman M M, et al. EBHI: a new enteroscope biopsy histopathological H&E image dataset for image classification evaluation. Phys Med, 2023, 107: 102534.
33. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
34. Liu Zhuang, Mao Hanzi, Wu Chaoyuan, et al. A ConvNet for the 2020s//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 11966-11976.
35. Wang Ao, Chen Hui, Lin Zijia, et al. LSNet: see large, focus small//2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2025: 9718-9729.
36. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale//9th International Conference on Learning Representations (ICLR). Virtual Event: OpenReview.net, 2021.
37. Liu Ze, Lin Yutong, Cao Yue, et al. Swin transformer: hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021: 9992-10002.
38. Sandler M, Howard A, Zhu Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 4510-4520.
39. Yue Yubiao, Li Zhenzhang. MedMamba: vision Mamba for medical image classification. arXiv, 2024: 2403.03849(v5).
40. Huo Xiangzuo, Sun Gang, Tian Shengwei, et al. HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed Signal Process Control, 2024, 87: 105534.

Journal of Biomedical Engineering

Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering

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