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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Pubblicato in:Cancers (Basel)
Autori principali: 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
Natura: Artigo
Lingua:Inglês
Pubblicazione: MDPI 2021
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Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC8003655/
https://ncbi.nlm.nih.gov/pubmed/33808802
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/cancers13061415
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