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Angelica I. Aviles-Rivero
Researcher at University of Cambridge
Publications - 69
Citations - 1177
Angelica I. Aviles-Rivero is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 10, co-authored 52 publications receiving 392 citations. Previous affiliations of Angelica I. Aviles-Rivero include University at Buffalo & George Washington University.
Papers
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Journal ArticleDOI
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael S. Roberts,Michael S. Roberts,Derek Driggs,Matthew Thorpe,Julian D. Gilbey,Michael Yeung,Stephan Ursprung,Angelica I. Aviles-Rivero,Christian Etmann,Cathal McCague,Lucian Beer,Jonathan R. Weir-McCall,Jonathan R. Weir-McCall,Zhongzhao Teng,Effrossyni Gkrania-Klotsas,James H.F. Rudd,Evis Sala,Carola-Bibiane Schönlieb +17 more
TL;DR: It is found that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases, which is a major weakness, given the urgency with which validated COVID-19 models are needed.
Journal ArticleDOI
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
TL;DR: A novel graph-based semi-supervised framework that uses a superpixel approach to define meaningful local regions in HSIs, which with high probability share the same classification label, resulting in accurate classifications when an incredibly small amount of labeled data is used.
Posted Content
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Kaixuan Wei,Angelica I. Aviles-Rivero,Jingwei Liang,Ying Fu,Carola-Bibiane Schönlieb,Hua Huang +5 more
TL;DR: This work presents a tuning-free PnP proximal algorithm, which can automatically determine the internal parameters including the penalty parameter, the denoising strength and the terminal time, and develops a policy network for automatic search of parameters which can be effectively learned via mixed model-free and model-based deep reinforcement learning.
Journal Article
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
Angelica I. Aviles-Rivero,Nicolas Papadakis,Ruoteng Li,Philip Sellars,Qingnan Fan,Robby T. Tan,Carola-Bibiane Schönlieb +6 more
TL;DR: This work introduces a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model, and is believed to be the first method that exploits graph- based semi- supervised learning forX-ray data classification.
Proceedings ArticleDOI
RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect
Ruoteng Li,Robby T. Tan,Loong-Fah Cheong,Angelica I. Aviles-Rivero,Qingnan Fan,Carola Bibiane Schoenlieb +5 more
TL;DR: A feature multiplier in a deep-learning based optical flow method is introduced that transforms the features of an image affected by the rain veiling effect into features that are less affected by it, which are called veiling-invariant features.