A Hybrid CNN-Transformer Model for Tumor-Infiltrating Lymphocyte Score Prediction in Breast Cancer Histopathological Image
Received: 24 October 2025 | Revised: 24 December 2025 | Accepted: 29 December 2025 | Online: 4 April 2026
Corresponding author: G. K. Shruthi
Abstract
Tumor-Infiltrating Lymphocytes (TILs) are crucial biomarkers in breast cancer, reflecting the immune response and providing prognostic information. Accurate quantification of TILs from whole-slide histopathology images remains challenging due to large image sizes, heterogeneous cellular distribution, and subtle morphological variations. To address these limitations, this study introduces a hybrid deep learning model, combining ResNet-152 and Vision Transformer (ViT) architectures, for automated TIL score prediction using the TIGER WSITILS dataset. Unlike conventional CNN-based approaches, which primarily capture local texture features, or transformer-only methods, which often lose fine-grained spatial detail, the proposed hybrid model leverages the strengths of both architectures—ResNet152 for rich local morphological representation and ViT for modeling long-range spatial relationships across tissue context. The model achieved a Mean Absolute Error (MAE) of 4.64, Mean Squared Error (MSE) of 48.21, and Root Mean Squared Error (RMSE) of 6.94 for regression-based prediction. In classification, it reached 94.74% accuracy with Pearson and Spearman correlation coefficients of 0.9894 and 0.9671, respectively. These results demonstrate that the proposed ResNet152-ViT hybrid framework effectively bridges local and global feature learning, offering improved accuracy, robustness, and interpretability for TIL assessment in breast cancer histopathology.
Keywords:
breast cancer, Tumor-Infiltrating Lymphocytes (TILs), ResNet152, Vision Transformer(ViT), Tiger dataset, TILs score predictionDownloads
References
Y. Zhao, J. Zhang, D. Hu, H. Qu, Y. Tian, and X. Cui, "Application of Deep Learning in Histopathology Images of Breast Cancer: A Review," Micromachines, vol. 13, no. 12, Dec. 2022. DOI: https://doi.org/10.3390/mi13122197
R. Gurumoorthy and M. Kamarasan, "Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12831–12836, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6720
R. Rashmi, K. Prasad, and C. B. K. Udupa, "Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review," Journal of Medical Systems, vol. 46, no. 1, Dec. 2021, Art. no. 7. DOI: https://doi.org/10.1007/s10916-021-01786-9
M. Tafavvoghi, L. A. Bongo, N. Shvetsov, L. T. R. Busund, and K. Møllersen, "Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review," Journal of Pathology Informatics, vol. 15, Dec. 2024, Art. no. 100363. DOI: https://doi.org/10.1016/j.jpi.2024.100363
B. Jiang, L. Bao, S. He, X. Chen, Z. Jin, and Y. Ye, "Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis," Breast Cancer Research, vol. 26, no. 1, Sept. 2024, Art. no. 137. DOI: https://doi.org/10.1186/s13058-024-01895-6
A. Fiorin, C. López Pablo, M. Lejeune, A. Hamza Siraj, and V. Della Mea, "Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets," Journal of Imaging Informatics in Medicine, vol. 37, no. 6, pp. 2996–3008, Dec. 2024. DOI: https://doi.org/10.1007/s10278-024-01043-8
C. Coleman et al., "Harnessing Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Opportunities and Barriers to Clinical Integration," International Journal of Molecular Sciences, vol. 26, no. 9, May 2025. DOI: https://doi.org/10.3390/ijms26094292
K. El Bairi et al., "The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group," npj Breast Cancer, vol. 7, no. 1, Dec. 2021, Art. no. 150.
R. Perera et al., "Annotation-efficient deep learning for breast cancer whole-slide image classification using tumour infiltrating lymphocytes and slide-level labels," Communications Engineering, vol. 3, no. 1, July 2024, Art. no. 104. DOI: https://doi.org/10.1038/s44172-024-00246-9
Z. Lu et al., "Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data," JCO Clinical Cancer Informatics, no. 4, pp. 480–490, May 2020. DOI: https://doi.org/10.1200/CCI.19.00126
M. Nasser and U. K. Yusof, "Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction," Diagnostics, vol. 13, no. 1, Jan. 2023. DOI: https://doi.org/10.3390/diagnostics13010161
M. Verdicchio, V. Brancato, C. Cavaliere, F. Isgrò, M. Salvatore, and M. Aiello, "A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images," Heliyon, vol. 9, no. 3, Mar. 2023. DOI: https://doi.org/10.1016/j.heliyon.2023.e14371
S. Choi et al., "Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer," npj Breast Cancer, vol. 9, no. 1, Aug. 2023, Art. no. 71. DOI: https://doi.org/10.1038/s41523-023-00577-4
S. Kim et al., "Improving Tumor-Infiltrating Lymphocytes Score Prediction in Breast Cancer with Self-Supervised Learning," Life, vol. 14, no. 1, Jan. 2024. DOI: https://doi.org/10.3390/life14010090
M. Yosofvand et al., "Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides," Cancers, vol. 15, no. 14, July 2023. DOI: https://doi.org/10.3390/cancers15143635
Z. Swiderska-Chadaj et al., "Learning to detect lymphocytes in immunohistochemistry with deep learning," Medical Image Analysis, vol. 58, Dec. 2019, Art. no. 101547. DOI: https://doi.org/10.1016/j.media.2019.101547
I. K. Evangeline, J. G. Precious, N. Pazhanivel, and S. P. A. Kirubha, "Automatic Detection and Counting of Lymphocytes from Immunohistochemistry Cancer Images Using Deep Learning," Journal of Medical and Biological Engineering, vol. 40, no. 5, pp. 735–747, Oct. 2020. DOI: https://doi.org/10.1007/s40846-020-00545-4
Z. Rauf, A. R. Khan, A. Sohail, H. Alquhayz, J. Gwak, and A. Khan, "Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN," Scientific Reports, vol. 13, no. 1, Aug. 2023, Art. no. 14047. DOI: https://doi.org/10.1038/s41598-023-40581-z
"TIGER - Grand Challenge," grand-challenge.org. https://tiger.grand-challenge.org/.
Z. Wang et al., "ResNet for Histopathologic Cancer Detection, the Deeper, the Better?," Journal of Data Science and Intelligent Systems, vol. 2, no. 4, pp. 212–220, 2024. DOI: https://doi.org/10.47852/bonviewJDSIS3202744
M. L. Abimouloud, K. Bensid, M. Elleuch, M. B. Ammar, and M. Kherallah, "Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images," Visual Computing for Industry, Biomedicine, and Art, vol. 8, no. 1, Jan. 2025, Art. no. 1. DOI: https://doi.org/10.1186/s42492-024-00181-8
Downloads
How to Cite
License
Copyright (c) 2026 G. K. Shruthi, Pushpa Ravikumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
