Vol. 5 No. 1 (2025): Issue 5
Articles

Bayesian Ridge Regression for Efficient Histopathology Slides Analysis

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  • Ivan Petrov,
  • Elena Sokolova,
  • Dmitry Ivanov
Ivan Petrov
Department of Computational Medicine, Yugra State University, Khanty-Mansiysk, 628012, Russia
Elena Sokolova
Center for Biomedical Data Analysis, Murmansk Arctic State University, Murmansk, 183038, Russia
Dmitry Ivanov
Laboratory of Applied Bioinformatics, Cherepovets State University, Cherepovets, 162611, Russia
DOI: https://doi.org/10.71070/es.v5i1.86

Published 2025-02-03

Keywords

  • Histopathology,
  • Image Analysis,
  • Bayesian Ridge Regression,
  • Diagnostic Accuracy,
  • High-Dimensional Data

How to Cite

Petrov, I., Sokolova, E., & Ivanov, D. (2025). Bayesian Ridge Regression for Efficient Histopathology Slides Analysis. Energy & System, 5(1). https://doi.org/10.71070/es.v5i1.86

Abstract

Histopathology slides analysis plays a crucial role in medical diagnosis and treatment decisions. However, the current research in this field faces challenges in accurately analyzing large-scale histopathology image data due to the complexity and heterogeneity of tissues. To address this issue, this paper proposes a novel approach utilizing Bayesian Ridge Regression for efficient histopathology slides analysis. By incorporating Bayesian techniques with ridge regression, our method not only enhances the accuracy of image analysis but also handles high-dimensional data effectively. This innovative framework contributes to improved diagnostic accuracy and efficiency in histopathology research, offering a promising solution to the existing limitations in the field.

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References

  1. P.-M. Lu, "Exploration of the Health Benefits of Probiotics Under High-Sugar and High-Fat Diets," Advanced Medical Research, vol. 2, no. 1, pp. 1-9, 2023.
  2. N. G. Maher et al., "Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin‐stained histopathology slides," Journal of the European Academy of Dermatology and Venereology, vol. 38, no. 12, pp. 2250-2258, 2024.
  3. D. Jin et al., "Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides," Nature Communications, vol. 15, no. 1, p. 3063, 2024.
  4. O. Krebs, S. Agarwal, and P. Tiwari, "Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors," in Medical Imaging 2023: Digital and Computational Pathology, 2023, vol. 12471: SPIE, p. 1247102.
  5. M. Unger and J. N. Kather, "A systematic analysis of deep learning in genomics and histopathology for precision oncology," BMC Medical Genomics, vol. 17, no. 1, p. 48, 2024.
  6. H. Xu, S. H. Lee, and T. H. Hwang, "Transfer learning for tumor mutation burden prediction and spatial heterogeneity analysis from histopathology slides in bladder cancer," Cancer Research, vol. 80, no. 16_Supplement, pp. 2111-2111, 2020.
  7. M. A. Tourni, "Texture analysis of histopathology slides for the prediction of EGFR gene mutation," 2019.
  8. T. Li et al., "[Retracted] Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study," Journal of Healthcare Engineering, vol. 2021, no. 1, p. 5972962, 2021.
  9. N. Tsiknakis et al., "Unveiling the power of model-agnostic multiscale analysis for enhancing artificial intelligence models in breast cancer histopathology images," IEEE Journal of Biomedical and Health Informatics, 2024.
  10. D. Kates-Harbeck et al., "Multimodal artificial intelligence models from baseline histopathology to predict prognosis in HR+ HER2-early breast cancer: Subgroup analysis," ed: American Society of Clinical Oncology, 2024.
  11. A. A. Schoepfer, R. Laplaza, M. D. Wodrich, J. Waser, and C. Corminboeuf, "Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity," ACS catalysis, vol. 14, no. 12, pp. 9302-9312, 2024.
  12. T. M. Almutiri, K. H. Alomar, and N. A. Alganmi, "Integrating multi-omics using bayesian ridge regression with iterative similarity bagging," Applied Sciences, vol. 14, no. 13, p. 5660, 2024.
  13. T. Almutiri, K. Alomar, and N. Alganmi, "Predicting drug response on multi-omics data using a hybrid of bayesian ridge regression with deep forest," International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, 2023.
  14. G. Xiang, Y. Ou, J. Chen, W. Wang, and H. Wu, "Identification of Hydrodynamic Coefficients of the SUBOFF Submarine Using the Bayesian Ridge Regression Model," Applied Sciences, vol. 13, no. 22, p. 12342, 2023.
  15. R. Vaish and U. Dwivedi, "Bayesian Ridge Regression for Power System Fault Localization," in 2024 5th International Conference for Emerging Technology (INCET), 2024: IEEE, pp. 1-5.
  16. J. Butler, M. Hjorth-Jensen, and J. G. Lietz, "Accelerating the convergence of coupled cluster calculations of the homogeneous electron gas using Bayesian ridge regression," The Journal of Chemical Physics, vol. 161, no. 13, 2024.
  17. M. Saqib, "Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model," Applied Intelligence, vol. 51, no. 5, pp. 2703-2713, 2021.
  18. J. Pal and B. Hong, "Applying Bayesian Ridge Regression AI Modeling in Virus Severity Prediction," arXiv preprint arXiv:2310.09485, 2023.
  19. W. Xu, X. Liu, F. Leng, and W. Li, "Blood-based multi-tissue gene expression inference with Bayesian ridge regression," Bioinformatics, vol. 36, no. 12, pp. 3788-3794, 2020.
  20. P.-M. Lu and Z. Zhang, "The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering," Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025.
  21. Y.-S. Cheng, P.-M. Lu, C.-Y. Huang, and J.-J. Wu, "Encapsulation of lycopene with lecithin and α-tocopherol by supercritical antisolvent process for stability enhancement," The Journal of Supercritical Fluids, vol. 130, pp. 246-252, 2017.