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arXiv · 1906.05956

Scalable Neural Architecture Search for 3D Medical Image Segmentation

Sungwoong Kim·Ildoo Kim·Sungbin Lim·Woonhyuk Baek·Chiheon Kim·Hyungjoo Cho·Boogeon Yoon·Taesup Kim
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Abstract

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space. Our NAS framework searches the structure of each layer including neural connectivities and operation types in both of the encoder and decoder. Since optimizing over a large discrete architecture space is difficult due to high-resolution 3D medical images, a novel stochastic sampling algorithm based on a continuous relaxation is also proposed for scalable gradient based optimization. On the 3D medical image segmentation tasks with a benchmark dataset, an automatically designed architecture by the proposed NAS framework outperforms the human-designed 3D U-Net, and moreover this optimized architecture is well suited to be transferred for different tasks.

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Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim. 2019-06-13. Scalable Neural Architecture Search for 3D Medical Image Segmentation. https://doi.org/10.1007/978-3-030-32248-9_25

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