Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

SIMPLE SUMMARY: This study evaluates the performance degradation of machine learning models for segmenting gliomas in brain magnetic resonance images caused by domain shift and proposed possible solutions. Although machine learning models exhibit significant potential for clinical applications, perf...

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Detalles Bibliográficos
Autores principales: Takahashi, Satoshi, Takahashi, Masamichi, Kinoshita, Manabu, Miyake, Mototaka, Kawaguchi, Risa, Shinojima, Naoki, Mukasa, Akitake, Saito, Kuniaki, Nagane, Motoo, Otani, Ryohei, Higuchi, Fumi, Tanaka, Shota, Hata, Nobuhiro, Tamura, Kaoru, Tateishi, Kensuke, Nishikawa, Ryo, Arita, Hideyuki, Nonaka, Masahiro, Uda, Takehiro, Fukai, Junya, Okita, Yoshiko, Tsuyuguchi, Naohiro, Kanemura, Yonehiro, Kobayashi, Kazuma, Sese, Jun, Ichimura, Koichi, Narita, Yoshitaka, Hamamoto, Ryuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003655/
https://www.ncbi.nlm.nih.gov/pubmed/33808802
http://dx.doi.org/10.3390/cancers13061415

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003655/
https://www.ncbi.nlm.nih.gov/pubmed/33808802
http://dx.doi.org/10.3390/cancers13061415

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