mlsanityData to 7 Oct 2026

NeRD: Neural Representation of Distribution for Medical Image Segmentation

arXiv 2103.04020 ↗First posted 6 Mar 2021eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution. Using NeRD, we propose an end-to-end deep learning model for medical image segmentation that can compensate the negative impact of feature distribution shifting issue caused by commonly used network operations such as padding and pooling. An implicit function is used to represent the parameter space of the feature distribution by querying the image coordinate. With NeRD, the impact of issues such as over-segmenting and missing have been reduced, and experimental results on the challenging white matter lesion segmentation and left atrial segmentation verify the effectiveness of the proposed method. The code is available via https://github.com/tinymilky/NeRD.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 6 Mar 2021.

Builds on

22 of 34 references.

References · 22

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10 more
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Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.

5 papers.

  1. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 88
  2. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 56
  3. 2022Reinforcement Learning with Neural Radiance Fieldscited by 43
  4. 2021Implicit Neural Representation Learning for Hyperspectral Image Super-Resolutioncited by 17
  5. 2024Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scanscited by 3