Kavli Affiliate: Cheng Peng
| First 5 Authors: Cheng Peng, Andriy Myronenko, Ali Hatamizadeh, Vish Nath, Md Mahfuzur Rahman Siddiquee
| Summary:
Semantic segmentation of 3D medical images is a challenging task due to the
high variability of the shape and pattern of objects (such as organs or
tumors). Given the recent success of deep learning in medical image
segmentation, Neural Architecture Search (NAS) has been introduced to find
high-performance 3D segmentation network architectures. However, because of the
massive computational requirements of 3D data and the discrete optimization
nature of architecture search, previous NAS methods require a long search time
or necessary continuous relaxation, and commonly lead to sub-optimal network
architectures. While one-shot NAS can potentially address these disadvantages,
its application in the segmentation domain has not been well studied in the
expansive multi-scale multi-path search space. To enable one-shot NAS for
medical image segmentation, our method, named HyperSegNAS, introduces a
HyperNet to assist super-net training by incorporating architecture topology
information. Such a HyperNet can be removed once the super-net is trained and
introduces no overhead during architecture search. We show that HyperSegNAS
yields better performing and more intuitive architectures compared to the
previous state-of-the-art (SOTA) segmentation networks; furthermore, it can
quickly and accurately find good architecture candidates under different
computing constraints. Our method is evaluated on public datasets from the
Medical Segmentation Decathlon (MSD) challenge, and achieves SOTA performances.
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