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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