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DC Field | Value | Language |
---|---|---|
dc.citation.conferencePlace | CC | - |
dc.citation.endPage | 228 | - |
dc.citation.startPage | 220 | - |
dc.citation.title | International Conference on Medical Image Computing and Computer Assisted Interventions | - |
dc.contributor.author | Kim, Sungwoo | - |
dc.contributor.author | Kim, Ildoo | - |
dc.contributor.author | Lim, Sungbin | - |
dc.contributor.author | Baek, Woonhyuk | - |
dc.contributor.author | Kim, Chiheon | - |
dc.contributor.author | Cho, Hyungjoo | - |
dc.contributor.author | Yoon, Boogeon | - |
dc.contributor.author | Kim, Taesup | - |
dc.date.accessioned | 2024-01-31T23:38:14Z | - |
dc.date.available | 2024-01-31T23:38:14Z | - |
dc.date.created | 2020-01-20 | - |
dc.date.issued | 2019-10-13 | - |
dc.description.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. | - |
dc.identifier.bibliographicCitation | International Conference on Medical Image Computing and Computer Assisted Interventions, pp.220 - 228 | - |
dc.identifier.doi | 10.1007/978-3-030-32248-9_25 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.scopusid | 2-s2.0-85075664424 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/79140 | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007%2F978-3-030-32248-9_25 | - |
dc.language | 영어 | - |
dc.publisher | MICCAI 2019 | - |
dc.title | Scalable Neural Architecture Search for 3D Medical Image Segmentation | - |
dc.type | Conference Paper | - |
dc.date.conferenceDate | 2019-10-13 | - |
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