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  1. 2025

  2. A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development

    Лыу, М. Ш. К., Бенедичук, М. В., Ропперт, Е. И., Кенжин, Р. М. & Тучинов, Б. Н., 18 Dec 2025, In: Journal of Imaging. 11, 12, 29 p., 454.

    Research output: Contribution to journal › Article › peer-review

  3. An Explanation Method for Semantic Segmentation Enhance Brain Tumor Classification

    Kenzhin, R., Luu, M. S. K., Pavlovskiy, E. & Tuchinov, B., 2025, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, p. 319-330 12 p. 23. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; vol. 15406 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  4. 2024

  5. Transfer Learning Approaches for Brain Metastases Screenings

    Luu, M. S. K., Tuchinov, B. N., Suvorov, V., Kenzhin, R. M., Amelina, E. V. & Letyagin, A. Y., Nov 2024, In: Biomedicines. 12, 11, 2561.

    Research output: Contribution to journal › Article › peer-review

  6. Discovering predictive features of multiple sclerosis from clinically isolated syndrome with machine learning

    Luu, M. S. K., Tuchinov, B. N., Prokaeva, A. I., Коробко, Д. С., Малкова, Н. А. & Tulupov, A. A., 24 Sept 2024, In: Artificial Intelligence in Health. 1, 4, p. 107-122 16 p.

    Research output: Contribution to journal › Article › peer-review

  7. Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

    Luu, M. S. K., Banerjee, S., Pavlovskiy, E. N. & Tuchinov, B. N., 2024, International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, p. 2320-2325 6 p. (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  8. 2022

  9. Binary Brain Tumor Classification With Semantic Features Using Convolutional Neural Network

    Лыу, М. Ш. К. & Pavlovskiy, E., 25 Oct 2022, Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022: 2022 USBEREIT. Institute of Electrical and Electronics Engineers Inc., p. 44-47 4 p. (Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  10. Cascaded Training Pipeline for 3D Brain Tumor Segmentation

    Luu, M. S. K. & Pavlovskiy, E., 2022, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. Crimi, A. & Bakas, S. (eds.). Springer, p. 410-420 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12962 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  11. Improving Brain Tumor Multiclass Classification With Semantic Features

    Sao Khue, L. M. & Pavlovskiy, E., 2022, Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022. Institute of Electrical and Electronics Engineers Inc., p. 150-154 5 p. (Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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