Abstract
We apply a method from Automated Machine Learning (AutoML), namely Neural Architecture Search (NAS), to the task of brain tumor segmentation in MRIs for the BraTS 2021 challenge. NAS methods are known to be compute-intensive, so we use a continuous and differentiable search space in order to apply a DiNTS search for optimal fully convolutional architectures. Our method obtained Dice scores of 0.9161, 0.8707 and 0.8537 for whole tumor, tumor core and enhancing tumor regions respectively on the test dataset, while requiring no manual design of the network architecture, which was found automatically from the provided training data.
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Milesi, A., Futrega, M., Marcinkiewicz, M., Ribalta, P. (2022). Brain Tumor Segmentation Using Neural Network Topology Search. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_31
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DOI: https://doi.org/10.1007/978-3-031-08999-2_31
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