The Experts below are selected from a list of 6039 Experts worldwide ranked by ideXlab platform
Taesup Kim - One of the best experts on this subject based on the ideXlab platform.
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MICCAI (3) - Scalable Neural Architecture Search for 3D Medical Image Segmentation
Lecture Notes in Computer Science, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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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Scalable Neural Architecture Search for 3D Medical Image Segmentation
arXiv: Learning, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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.
Sungwoong Kim - One of the best experts on this subject based on the ideXlab platform.
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MICCAI (3) - Scalable Neural Architecture Search for 3D Medical Image Segmentation
Lecture Notes in Computer Science, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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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Scalable Neural Architecture Search for 3D Medical Image Segmentation
arXiv: Learning, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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.
Xiaoyu Qiu - One of the best experts on this subject based on the ideXlab platform.
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nas unet neural architecture search for Medical Image Segmentation
IEEE Access, 2019Co-Authors: Yu Weng, Tianbao Zhou, Xiaoyu QiuAbstract:Neural architecture search (NAS) has significant progress in improving the accuracy of Image classification. Recently, some works attempt to extend NAS to Image Segmentation which shows preliminary feasibility. However, all of them focus on searching architecture for semantic Segmentation in natural scenes. In this paper, we design three types of primitive operation set on search space to automatically find two cell architecture DownSC and UpSC for semantic Image Segmentation especially Medical Image Segmentation. Inspired by the U-net architecture and its variants successfully applied to various Medical Image Segmentation, we propose NAS-Unet which is stacked by the same number of DownSC and UpSC on a U-like backbone network. The architectures of DownSC and UpSC updated simultaneously by a differential architecture strategy during the search stage. We demonstrate the good Segmentation results of the proposed method on Promise12, Chaos, and ultrasound nerve datasets, which collected by magnetic resonance imaging, computed tomography, and ultrasound, respectively. Without any pretraining, our architecture searched on PASCAL VOC2012, attains better performances and much fewer parameters (about 0.8M) than U-net and one of its variants when evaluated on the above three types of Medical Image datasets.
Chiheon Kim - One of the best experts on this subject based on the ideXlab platform.
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MICCAI (3) - Scalable Neural Architecture Search for 3D Medical Image Segmentation
Lecture Notes in Computer Science, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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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Scalable Neural Architecture Search for 3D Medical Image Segmentation
arXiv: Learning, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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.
Hyungjoo Cho - One of the best experts on this subject based on the ideXlab platform.
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MICCAI (3) - Scalable Neural Architecture Search for 3D Medical Image Segmentation
Lecture Notes in Computer Science, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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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Scalable Neural Architecture Search for 3D Medical Image Segmentation
arXiv: Learning, 2019Co-Authors: Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup KimAbstract: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.