92 citations · 193 across the 8 of their papers we have counts for
4 papers · 1 filter
GraphX Chest X-Ray Classification Under Extreme Minimal Supervision
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the crit…
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has m…
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
Philip Sellars, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data. In this paper we present a novel gra…
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis +3
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral cova…