Search Results for author: Angelica I. Aviles-Rivero

Found 17 papers, 2 papers with code

Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

no code implementations4 Apr 2022 Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb

We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.

Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings

no code implementations21 Jul 2021 Dominik Kloepfer, Angelica I. Aviles-Rivero, Daniel Heydecker

Firstly, we prove that, under some weak assumptions, vertex embeddings derived from random walks do indeed converge both in the single limit of the number of random walks $N \to \infty$ and in the double limit of both $N$ and the length of each random walk $L\to\infty$.

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

1 code implementation8 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.

Semi-Supervised Image Classification

HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

1 code implementation7 Jun 2021 Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.


GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays

no code implementations30 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.

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

no code implementations13 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.

General Classification Image Classification +1

Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video Recovery

no code implementations17 Dec 2019 Samar M. Alsaleh, Angelica I. Aviles-Rivero, Noemie Debroux, James K. Hahn

Our solution is a twostep approach that allows for both detection and restoration of the damaged regions on video data.

Dynamic Spectral Residual Superpixels

no code implementations10 Oct 2019 Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb

We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.


Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal

no code implementations29 May 2018 Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk

Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.

Reflection Removal

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