Philip Sellars

IEEE Trans. Neural Networks Learn. Syst., April, 2024

2022

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays.

[BibT_eX]

[DOI]

Nicolas Papadakis

Pattern Recognit., 2022

2021

LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification.

[BibT_eX]

[DOI]

IEEE Trans. Geosci. Remote. Sens., 2020

The GraphNet Zoo: A Plug-and-Play Framework for Deep Semi-Supervised Classification.

[BibT_eX]

[DOI]

Marianne de Vriendt

CoRR, 2020

Two Cycle Learning: Clustering Based Regularisation for Deep Semi-Supervised Classification.

[BibT_eX]

[DOI]

CoRR, 2020

The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification.

[BibT_eX]

[DOI]

Marianne de Vriendt

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 2020

2019

GraphX<sup>NET</sup>-Chest X-Ray Classification Under Extreme Minimal Supervision.

[BibT_eX]

[DOI]

CoRR, 2019

GraphX $$^\mathbf{\small NET } -$$ -Chest X-Ray Classification Under Extreme Minimal Supervision.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Semi-Supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification.

[BibT_eX]

[DOI]

Nicolas Papadakis

David Coomes

Anita Faul