Coordinate Independent Convolutional Networks--Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
M Weiler, P Forré, E Verlinde, M Welling - ar** accurate neural network-based computer-aided diagnosis systems …
Achieving rotational invariance with bessel-convolutional neural networks
For many applications in image analysis, learning models that are invariant to translations
and rotations is paramount. This is the case, for example, in medical imaging where the …
and rotations is paramount. This is the case, for example, in medical imaging where the …
Self-interpretable model with transformation equivariant interpretation
With the proliferation of machine learning applications in the real world, the demand for
explaining machine learning predictions continues to grow especially in high-stakes fields …
explaining machine learning predictions continues to grow especially in high-stakes fields …
Few-shot shape recognition by learning deep shape-aware features
Traditional shape descriptors have been gradually replaced by convolutional neural
networks due to their superior performance in feature extraction and classification. The state …
networks due to their superior performance in feature extraction and classification. The state …
Adoption of image-driven machine learning for microstructure characterization and materials design: A perspective
The recent surge in the adoption of machine learning techniques for materials design,
discovery, and characterization has resulted in increased interest in and application of …
discovery, and characterization has resulted in increased interest in and application of …
Breast cancer classification using equivariance transition in group convolutional neural networks
In computer vision, rotation equivariance and translation invariance are properties of a
representation that preserve the geometric structure of a transformed input. These properties …
representation that preserve the geometric structure of a transformed input. These properties …
An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
A key step in computational pathology is to automate the laborious process of manual nuclei
segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite …
segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite …
Practical equivariances via relational conditional neural processes
Abstract Conditional Neural Processes (CNPs) are a class of metalearning models popular
for combining the runtime efficiency of amortized inference with reliable uncertainty …
for combining the runtime efficiency of amortized inference with reliable uncertainty …