TY - GEN
T1 - AutoGAN-Synthesizer
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
AU - Hu, Xiaobin
AU - Shen, Ruolin
AU - Luo, Donghao
AU - Tai, Ying
AU - Wang, Chengjie
AU - Menze, Bjoern H.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Considering the difficulty to obtain complete multi-modality MRI scans in some real-world data acquisition situations, synthesizing MRI data is a highly relevant and important topic to complement diagnosis information in clinical practice. In this study, we present a novel MRI synthesizer, called AutoGAN-Synthesizer, which automatically discovers generative networks for cross-modality MRI synthesis. Our AutoGAN-Synthesizer adopts gradient-based search strategies to explore the generator architecture by determining how to fuse multi-resolution features and utilizes GAN-based perceptual searching losses to handle the trade-off between model complexity and performance. Our AutoGAN-Synthesizer can search for a remarkable and light-weight architecture with 6.31 Mb parameters only occupying 12 GPU hours. Moreover, to incorporate richer prior knowledge for MRI synthesis, we derive K-space features containing the low- and high-spatial frequency information and incorporate such features into our model. To our best knowledge, this is the first work to explore AutoML for cross-modality MRI synthesis, and our approach is also capable of tailoring networks given either different multiple modalities or just a single modality as input. Extensive experiments show that our AutoGAN-Synthesizer outperforms the state-of-the-art MRI synthesis methods both quantitatively and qualitatively. The code are available at https://github.com/HUuxiaobin/AutoGAN-Synthesizer.
AB - Considering the difficulty to obtain complete multi-modality MRI scans in some real-world data acquisition situations, synthesizing MRI data is a highly relevant and important topic to complement diagnosis information in clinical practice. In this study, we present a novel MRI synthesizer, called AutoGAN-Synthesizer, which automatically discovers generative networks for cross-modality MRI synthesis. Our AutoGAN-Synthesizer adopts gradient-based search strategies to explore the generator architecture by determining how to fuse multi-resolution features and utilizes GAN-based perceptual searching losses to handle the trade-off between model complexity and performance. Our AutoGAN-Synthesizer can search for a remarkable and light-weight architecture with 6.31 Mb parameters only occupying 12 GPU hours. Moreover, to incorporate richer prior knowledge for MRI synthesis, we derive K-space features containing the low- and high-spatial frequency information and incorporate such features into our model. To our best knowledge, this is the first work to explore AutoML for cross-modality MRI synthesis, and our approach is also capable of tailoring networks given either different multiple modalities or just a single modality as input. Extensive experiments show that our AutoGAN-Synthesizer outperforms the state-of-the-art MRI synthesis methods both quantitatively and qualitatively. The code are available at https://github.com/HUuxiaobin/AutoGAN-Synthesizer.
KW - Cross-modality MRI synthesis
KW - Generative adversarial networks
KW - Neural architecture search
UR - http://www.scopus.com/inward/record.url?scp=85139160776&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16446-0_38
DO - 10.1007/978-3-031-16446-0_38
M3 - Conference contribution
AN - SCOPUS:85139160776
SN - 9783031164453
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 397
EP - 409
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2022 through 22 September 2022
ER -