• Corpus ID: 211032170

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series

@article{Tachibana2020ANN,
  title={A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series},
  author={Yasuhiko Tachibana and Masataka Nishimori and Naoyuki Kitamura and Kensuke Umehara and Junko Ota and Takayuki Obata and Tatsuya Higashi},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.01891}
}
Purpose: To construct a neural network model that can learn the different diagnosing strategies of radiologists to better classify aneurysm status in magnetic resonance angiography images. Materials and methods: This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series (subjects: male 1843 [mean age, 50.2 +/- 11.7 years], female 1580 [50.8 +/- 11.3 years]) recorded from November 2017 through January 2019. The image series were read independently for… 
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