1 code implementation • 8 Jun 2021 • Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect.
no code implementations • 30 Sep 2020 • Angelica I. Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb, Nicolas Papadakis
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
no code implementations • 13 Mar 2020 • Marianne de Vriendt, Philip Sellars, Angelica I. Aviles-Rivero
In this work, we propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches, which up to our knowledge it is the first time that an approach with minimal labels has been shown to such an unprecedented scale with medical data.
1 code implementation • 15 Jan 2020 • Philip Sellars, Angelica Aviles-Rivero, Carola Bibiane Schönlieb
Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation.
no code implementations • 23 Jul 2019 • Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Qingnan Fan, Robby T. Tan, Carola-Bibiane Schönlieb
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
no code implementations • 20 Jun 2019 • Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Samar M Alsaleh, Robby T Tan, Carola-Bibiane Schönlieb
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.
1 code implementation • 14 Mar 2019 • 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.
no code implementations • 14 Jan 2019 • Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, Carola-Bibane Schönlieb
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.