no code implementations • 23 Oct 2023 • Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, Laurent Heutte
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning.
no code implementations • 30 Jan 2023 • Hugo Lerogeron, Romain Picot-Clemente, Alain Rakotomamonjy, Laurent Heutte
Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series.
no code implementations • 4 Aug 2022 • Caio da S. Dias, Alceu de S. Britto Jr., Jean P. Barddal, Laurent Heutte, Alessandro L. Koerich
This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents.
1 code implementation • 17 Aug 2020 • Lucca Portes Cavalheiro, Jean Paul Barddal, Alceu de Souza Britto Jr, Laurent Heutte
Mining data streams is a challenge per se.
no code implementations • 16 Jul 2020 • Simon Bernard, Hongliu Cao, Robert Sabourin, Laurent Heutte
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions.
no code implementations • 6 Jul 2020 • Hongliu Cao, Simon Bernard, Robert Sabourin, Laurent Heutte
Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task.
no code implementations • 22 Jul 2019 • Kelly Lais Wiggers, Alceu de Souza Britto Junior, Alessandro Lameiras Koerich, Laurent Heutte, Luiz Eduardo Soares de Oliveira
This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning.
no code implementations • 22 Jun 2019 • Kelly L. Wiggers, Alceu S. Britto Jr., Laurent Heutte, Alessandro L. Koerich, Luiz S. Oliveira
This paper presents a novel approach for image retrieval and pattern spotting in document image collections.
no code implementations • 20 Jun 2019 • Ignacio Úbeda, Jose M. Saavedra, Stéphane Nicolas, Caroline Petitjean, Laurent Heutte
Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query.
no code implementations • 20 Jun 2018 • Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients.
no code implementations • 29 Mar 2018 • Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning.
no code implementations • 12 Mar 2018 • Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.