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Coordinate Independent Convolutional Networks--Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …
Effective rotation-invariant point cnn with spherical harmonics kernels
We present a novel rotation invariant architecture operating directly on point cloud data. We
demonstrate how rotation invariance can be injected into a recently proposed point-based …
demonstrate how rotation invariance can be injected into a recently proposed point-based …
[HTML][HTML] Roto-translation equivariant convolutional networks: Application to histopathology image analysis
Rotation-invariance is a desired property of machine-learning models for medical image
analysis and in particular for computational pathology applications. We propose a …
analysis and in particular for computational pathology applications. We propose a …
Rotation invariance and equivariance in 3D deep learning: a survey
J Fei, Z Deng - Artificial Intelligence Review, 2024 - Springer
Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …
Standardised convolutional filtering for radiomics
A Depeursinge, V Andrearczyk, P Whybra… - arxiv preprint arxiv …, 2020 - arxiv.org
The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of
radiomics studies by standardising the computational process of extracting image …
radiomics studies by standardising the computational process of extracting image …
Neural networks enforcing physical symmetries in nonlinear dynamical lattices: The case example of the Ablowitz–Ladik model
In this work we introduce symmetry-preserving, physics-informed neural networks (S-PINNs)
motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices …
motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices …
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI
Abstract Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess
brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very …
brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very …