What type of study is this?

This is a Quantitative Study study.

September 29, 2025Open Access

Rep3D: Re-parameterize Large 3D Kernels with Low-Rank Receptive Modeling for Medical Imaging

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Authors

HLHo Hin LeeChinese University of Hong Kong QLQuan LiuUniversity of Science and Technology of China SBShunxing BaoVanderbilt University

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Implication

Rep3D demonstrates improved optimization stability and segmentation accuracy in medical imaging, highlighting the utility of learnable spatial priors.

Key Points

Rep3D improves segmentation accuracy in 3D medical imaging by effectively re-parameterizing large kernels.
The two-stage modulation network enhances kernel updates through learnable spatial priors, stabilizing optimization.
Evaluation on five segmentation benchmarks shows consistent gains over traditional methods, including transformer architectures.
Innovative design bypasses multi-branch complexity while ensuring efficiency and scalability in 3D imaging tasks.

Cite This Study

Lee et al. (2025) studied this question.

synapsesocial.com/papers/68da5a3ec1728099cfd11a47 https://doi.org/10.48550/arxiv.2505.19603

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Implication

Rep3D demonstrates improved optimization stability and segmentation accuracy in medical imaging, highlighting the utility of learnable spatial priors.

Rep3D: Re-parameterize Large 3D Kernels with Low-Rank Receptive Modeling for Medical Imaging

Discussion

Member takes

Implication

Key Points

Cite This Study

Rep3D: Re-parameterize Large 3D Kernels with Low-Rank Receptive Modeling for Medical Imaging

Discussion

Member takes

Implication

Key Points

Cite This Study

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