Scalable Neural Architecture Search for 3D Medical Image Segmentation

MICCAI (2019)


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.


김성웅 (카카오브레인), 김일두 (카카오브레인), 임성빈 (카카오브레인), 백운혁 (카카오브레인), 김치헌 (카카오브레인), 조형주 (서울대학교), 윤부근 (카카오브레인), 김태섭 (MILA)


meta-learning medical

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