Version 1
: Received: 21 October 2024 / Approved: 22 October 2024 / Online: 24 October 2024 (11:55:34 CEST)
How to cite:
Luu, M. S. K.; Tuchinov, B. N.; Suvorov, V.; Kenzhin, R. M.; Amelina, E. V.; Letyagin, A. Y. Transfer Learning Approaches for Brain Metastases Screenings. Preprints2024, 2024101738. https://doi.org/10.20944/preprints202410.1738.v1
Luu, M. S. K.; Tuchinov, B. N.; Suvorov, V.; Kenzhin, R. M.; Amelina, E. V.; Letyagin, A. Y. Transfer Learning Approaches for Brain Metastases Screenings. Preprints 2024, 2024101738. https://doi.org/10.20944/preprints202410.1738.v1
Luu, M. S. K.; Tuchinov, B. N.; Suvorov, V.; Kenzhin, R. M.; Amelina, E. V.; Letyagin, A. Y. Transfer Learning Approaches for Brain Metastases Screenings. Preprints2024, 2024101738. https://doi.org/10.20944/preprints202410.1738.v1
APA Style
Luu, M. S. K., Tuchinov, B. N., Suvorov, V., Kenzhin, R. M., Amelina, E. V., & Letyagin, A. Y. (2024). Transfer Learning Approaches for Brain Metastases Screenings. Preprints. https://doi.org/10.20944/preprints202410.1738.v1
Chicago/Turabian Style
Luu, M. S. K., Evgeniya V. Amelina and Andrey Yu. Letyagin. 2024 "Transfer Learning Approaches for Brain Metastases Screenings" Preprints. https://doi.org/10.20944/preprints202410.1738.v1
Abstract
Brain metastasis is a severe and complicated cancer that requires timely screening and follow-up approaches using precise segmentation techniques for effective surgical intervention, radiation therapy, or monitoring the disease progression. In this study, we explore the efficacy of transfer learning in improving the automatic segmentation of brain metastases on Magnetic Resonance Imaging scans with the subsequent possibility of using it in clinical practice for preventive examinations and remote diagnostics. We train three deep learning models on a public dataset from the Brain Tumor Segmentation 2024 Challenge, then fine-tune these pretrained models on a small private dataset and compare them to models trained on private data from scratch. The results indicate that models utilizing transfer learning achieve superior accuracy and generalization in segmenting brain metastases. We also observed that the custom loss function significantly enhances performance compared to the default configuration. This study highlights the importance of leveraging transfer learning in medical imaging to address challenges associated with small, specialized datasets.
Keywords
transfer learning; brain metastases; segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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