3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis

by   Valentin Oreiller, et al.

Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to the discrete rotation of local descriptors, such as Local Binary Patterns (LBP) or the Scale Invariant Feature Transform (SIFT). Other strategies achieve this invariance using Laplacian of Gaussian or steerable wavelets for instance, preventing the introduction of sampling errors during the discretization of the rotations. In this work, we obtain LRI operators via the local projection of the image on the spherical harmonics basis, followed by the computation of the bispectrum, which shares and extends the invariance properties of the spectrum. 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. The performance of each design is evaluated on two datasets and compared against a standard 3D CNN. The first dataset is made of 3D volumes composed of synthetically generated rotated patterns, while the second contains malignant and benign pulmonary nodules in Computed Tomography (CT) images. The results indicate that bispectrum CNNs allows for a significantly better characterization of 3D textures than both the spectral and standard CNN. In addition, it can efficiently learn with fewer training examples and trainable parameters when compared to a standard convolutional layer.



There are no comments yet.


page 1

page 3

page 7


Local Rotation Invariance in 3D CNNs

Locally Rotation Invariant (LRI) image analysis was shown to be fundamen...

Learning rotation invariant convolutional filters for texture classification

We present a method for learning discriminative filters using a shallow ...

PDO-eS^2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs

Spherical signals exist in many applications, e.g., planetary data, LiDA...

Geometric Operator Convolutional Neural Network

The Convolutional Neural Network (CNN) has been successfully applied in ...

Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

In the past three decades, a large number of metaheuristics have been pr...

Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a co...

Rotational 3D Texture Classification Using Group Equivariant CNNs

Convolutional Neural Networks (CNNs) traditionally encode translation eq...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.