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Multi-view Attention for gestational age at birth prediction

arXiv 2207.04130 ↗First posted 8 Jul 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

We present our method for gestational age at birth prediction for the SLCN (surface learning for clinical neuroimaging) challenge. Our method is based on a multi-view shape analysis technique that captures 2D renderings of a 3D object from different viewpoints. We render the brain features on the surface of the sphere and then the 2D images are analyzed via 2D CNNs and an attention layer for the regression task. The regression task achieves a MAE of 1.637 +- 1.3 on the Native space and MAE of 1.38 +- 1.14 on the template space. The source code for this project is available in our github repository https://github.com/MathieuLeclercq/SLCN_challenge_UNC

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 8 Jul 2022.

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References · 7

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  4. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  5. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  6. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  7. 2017O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysiscited by 47

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