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Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11384)

Abstract

We introduce a new family of classifiers based on our previous DeepSCAN architecture, in which densely connected blocks of dilated convolutions are embedded in a shallow U-net-style structure of down/upsampling and skip connections. These networks are trained using a newly designed loss function which models label noise and uncertainty. We present results on the testing dataset of the Multimodal Brain Tumor Segmentation Challenge 2018.

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McKinley, R., Meier, R., Wiest, R. (2019). Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_40

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  • DOI: https://doi.org/10.1007/978-3-030-11726-9_40

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  • Print ISBN: 978-3-030-11725-2

  • Online ISBN: 978-3-030-11726-9

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