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Found 8 papers, 4 papers with code
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching
In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures.
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
In this work, we propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model that adapts its normalization layers based on the input type, trained with non-registered interleaved mixed data.
Heuristic Hyperparameter Choice for Image Anomaly Detection
Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality.
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a. k. a.
Giga-SSL: Self-Supervised Learning for Gigapixel Images
Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice.
[Reproducibility Report] Explainable Deep One-Class Classification
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods.
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivial 3D images.
Ranked #1 on
2D Semantic Segmentation
on GF-PA66 3D XCT
Dealing with Topological Information within a Fully Convolutional Neural Network
A fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information.
