Local Rotation Invariance in 3D CNNs

• URL: http://arxiv.org/abs/2003.08890v1
• Date: Thu, 19 Mar 2020 16:24:49 GMT
• Title: Local Rotation Invariance in 3D CNNs
• Authors: Vincent Andrearczyk, Julien Fageot, Valentin Oreiller, Xavier Montet, Adrien Depeursinge
• Abstract summary: Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with data augmentation.
• Score: 3.579867431007686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the cornerstone of several breakthroughs in texture analysis, including Local Binary Patterns (LBP), Maximum Response 8 (MR8) and steerable filterbanks. Whereas globally rotation invariant Convolutional Neural Networks (CNN) were recently proposed, LRI was very little investigated in the context of deep learning. LRI designs allow learning filters accounting for all orientations, which enables a drastic reduction of trainable parameters and training data when compared to standard 3D CNNs. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. Two methods use orientation channels (responses to rotated kernels), either by explicitly rotating the kernels or using steerable filters. These orientation channels constitute a locally rotation equivariant representation of the data. Local pooling across orientations yields LRI image analysis. Steerable filters are used to achieve a fine and efficient sampling of 3D rotations as well as a reduction of trainable parameters and operations, thanks to a parametric representations involving solid Spherical Harmonics (SH), which are products of SH with associated learned radial profiles.Finally, we investigate a third strategy to obtain LRI based on rotational invariants calculated from responses to a learned set of solid SHs. The proposed methods are evaluated and compared to standard CNNs on 3D datasets including synthetic textured volumes composed of rotated patterns, and pulmonary nodule classification in CT. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with data augmentation.

Related papers

• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• $E(3) \times SO(3)$-Equivariant Networks for Spherical Deconvolution in Diffusion MRI [4.726777092009554]
  We present a framework for sparse deconvolution of volumes where each voxel contains a spherical signal. This work constructs equivariant deep learning layers which respect to symmetries of spatial rotations, reflections, and translations.
  arXiv  Detail & Related papers  (2023-04-12T18:37:32Z)
• RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network [56.42518353373004]
  We propose a new convolutional operation, called Rotation-Invariant Coordinate Convolution (RIC-C) By replacing all standard convolutional layers in a CNN with the corresponding RIC-C, a RIC-CNN can be derived. It can be observed that RIC-CNN achieves the state-of-the-art classification on the rotated test dataset of MNIST.
  arXiv  Detail & Related papers  (2022-11-21T19:27:02Z)
• SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement [118.20816888815658]
  We propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded Selective Position variant' procedure relies on an attention mechanism that can effectively attend to the underlying rotation condition of the input. We demonstrate the merits of the SPE-Net and the associated hypothesis on four benchmarks, showing evident improvements on both rotated and unrotated test data over SOTA methods.
  arXiv  Detail & Related papers  (2022-11-15T15:59:09Z)
• How can spherical CNNs benefit ML-based diffusion MRI parameter estimation? [2.4417196796959906]
  Spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) Current clinical practice commonly acquires dMRI data consisting of only 6 diffusion weighted images (DWIs)
  arXiv  Detail & Related papers  (2022-07-01T17:49:26Z)
• Rotation-Equivariant Deep Learning for Diffusion MRI [49.321304988619865]
  Convolutional networks are successful, but they have recently been outperformed by new neural networks that are equivariant under rotations and translations. Here we generalize them to 6D diffusion MRI data, ensuring joint equivariance under 3D roto-translations in image space and the matching 3D rotations in $q$-space. Our proposed neural networks yield better results and require fewer scans for training compared to non-rotation-equivariant deep learning.
  arXiv  Detail & Related papers  (2021-02-13T15:18:34Z)
• Enhancing Fiber Orientation Distributions using convolutional Neural Networks [0.0]
  We learn improved FODs for commercially acquired MRI. We evaluate patch-based 3D convolutional neural networks (CNNs) Our approach may enable robust CSD model estimation on single-shell dMRI acquisition protocols.
  arXiv  Detail & Related papers  (2020-08-12T16:06:25Z)
• 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis [3.579867431007686]
  Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis. We investigate the benefits of using the bispectrum over the spectrum in the design of a LRI layer embedded in a shallow Convolutional Neural Network (CNN) for 3D image analysis.
  arXiv  Detail & Related papers  (2020-04-28T09:01:13Z)
• Learning Local Neighboring Structure for Robust 3D Shape Representation [143.15904669246697]
  Representation learning for 3D meshes is important in many computer vision and graphics applications. We propose a local structure-aware anisotropic convolutional operation (LSA-Conv) Our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-21T13:40:03Z)
• Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images [3.053417311299492]
  Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. Dense Steerable Filter CNNs (DSF-CNNs) use group convolutions with multiple rotated copies of each filter in a densely connected framework. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of pathology computational.
  arXiv  Detail & Related papers  (2020-04-06T23:12:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.