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
International audienceRecently, the in-vivo imaging of pulmonary alveoli was made possible thanks to...
Supervised learning introduces genericity in the field of image classification, thus enabling fast p...
Machine learning is an exciting and widely used field in computational world. In this work multiclas...
We illustrate the potential of our image classification method on three datasets of images at differ...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
peer reviewedWe present a unified framework involving the extraction of random subwindows within im...
In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classific...
This paper considers the general problem of image classification without using any prior kn...
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...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
peer reviewedThis paper addresses image annotation, i.e. labelling pixels of an image with a class a...
<p><b>Copyright information:</b></p><p>Taken from "Random subwindows and extremely randomized trees ...
The large size of histological images combined with their very challenging appearance are two main d...
International audienceThelargesizeofhistologicalimagescombinedwiththeirvery challenging appearance a...
International audienceRecently, the in-vivo imaging of pulmonary alveoli was made possible thanks to...
Supervised learning introduces genericity in the field of image classification, thus enabling fast p...
Machine learning is an exciting and widely used field in computational world. In this work multiclas...
We illustrate the potential of our image classification method on three datasets of images at differ...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
peer reviewedWe present a unified framework involving the extraction of random subwindows within im...
In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classific...
This paper considers the general problem of image classification without using any prior kn...
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...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
peer reviewedThis paper addresses image annotation, i.e. labelling pixels of an image with a class a...
<p><b>Copyright information:</b></p><p>Taken from "Random subwindows and extremely randomized trees ...
The large size of histological images combined with their very challenging appearance are two main d...
International audienceThelargesizeofhistologicalimagescombinedwiththeirvery challenging appearance a...
International audienceRecently, the in-vivo imaging of pulmonary alveoli was made possible thanks to...
Supervised learning introduces genericity in the field of image classification, thus enabling fast p...
Machine learning is an exciting and widely used field in computational world. In this work multiclas...