Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

Maloca et al. implement convolutional neural network (CNN) to automatically segment OCT images obtained from cynomolgus monkeys. The results are compared to annotations generated by human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visuali...

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主要な著者:	Peter M. Maloca, Philipp L. Müller, Aaron Y. Lee, Adnan Tufail, Konstantinos Balaskas, Stephanie Niklaus, Pascal Kaiser, Susanne Suter, Javier Zarranz-Ventura, Catherine Egan, Hendrik P. N. Scholl, Tobias K. Schnitzer, Thomas Singer, Pascal W. Hasler, Nora Denk
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2021-02-01
シリーズ:	Communications Biology
オンライン･アクセス:	https://doi.org/10.1038/s42003-021-01697-y
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