Add to ORKGWe illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based on random subwindows extraction and the combination of their classification using ensembles of extremely randomized decision trees.
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
Biomedical research has been revolutionised by the development of high throughput systems which enab...
The large size of histological images combined with their very challenging appearance are two main d...
We illustrate the potential of our image classification method on three datasets of images at differ...
We illustrate the potential of our image classification method on three datasets of images at differ...
peer reviewedWe present a unified framework involving the extraction of random subwindows within im...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
We present a novel, generic image classification method based on a recent machine learning algorithm...
We present a novel, generic image classification method based on a recent machine learning algorithm...
International audienceRecently, the in-vivo imaging of pulmonary alveoli was made possible thanks to...
The work presented in this thesis is motivated by the problem of automatic image classification. Ima...
This paper considers the general problem of image classification without using any prior kn...
In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classific...
Random forest is a popular machine learning algorithm which is made up of an ensemble of decision tr...
Inspired by the use of random projections in biological sensing systems, we present a new algorithm ...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
Biomedical research has been revolutionised by the development of high throughput systems which enab...
The large size of histological images combined with their very challenging appearance are two main d...
We illustrate the potential of our image classification method on three datasets of images at differ...
We illustrate the potential of our image classification method on three datasets of images at differ...
peer reviewedWe present a unified framework involving the extraction of random subwindows within im...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
We present a novel, generic image classification method based on a recent machine learning algorithm...
We present a novel, generic image classification method based on a recent machine learning algorithm...
International audienceRecently, the in-vivo imaging of pulmonary alveoli was made possible thanks to...
The work presented in this thesis is motivated by the problem of automatic image classification. Ima...
This paper considers the general problem of image classification without using any prior kn...
In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classific...
Random forest is a popular machine learning algorithm which is made up of an ensemble of decision tr...
Inspired by the use of random projections in biological sensing systems, we present a new algorithm ...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
Biomedical research has been revolutionised by the development of high throughput systems which enab...
The large size of histological images combined with their very challenging appearance are two main d...