Add to ORKGIn this paper we describe our experiments related to the ImageCLEF 2010 medical modality classification task using extremely randomized trees. Our best run combines bags of textual and visual features. It yields 90% recognition rate and ranks 6th among 45 runs (ranging from 94% downto 12%)
In particular medical imaging data, such as positron emission tomography (PET), computed tomography ...
International audienceMedical imaging protocols produce large amounts of multi- modal volumetric ima...
International audienceThis year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual...
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...
Searching for medical image content is a regular task for many physicians, especially in radiology. ...
Large datasets of unlabelled medical images are increasingly becoming available; however only a smal...
Imaging modality can aid retrieval of medical images for clinical practice, research, and education....
We describe an approach for the automatic modality classification in medical image retrieval task of...
This paper presents the modelling approaches performed to automatically predict the modality of imag...
Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed ...
We illustrate the potential of our image classification method on three datasets of images at differ...
This paper presents the modelling approaches performed to automatically predict the modality of imag...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
In particular medical imaging data, such as positron emission tomography (PET), computed tomography ...
International audienceMedical imaging protocols produce large amounts of multi- modal volumetric ima...
International audienceThis year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual...
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...
Searching for medical image content is a regular task for many physicians, especially in radiology. ...
Large datasets of unlabelled medical images are increasingly becoming available; however only a smal...
Imaging modality can aid retrieval of medical images for clinical practice, research, and education....
We describe an approach for the automatic modality classification in medical image retrieval task of...
This paper presents the modelling approaches performed to automatically predict the modality of imag...
Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed ...
We illustrate the potential of our image classification method on three datasets of images at differ...
This paper presents the modelling approaches performed to automatically predict the modality of imag...
Background: With the improvements in biosensors and high-throughput image acquisition technologies, ...
peer reviewedIn this paper, we address a problem of biomedical image classification that involves th...
In particular medical imaging data, such as positron emission tomography (PET), computed tomography ...
International audienceMedical imaging protocols produce large amounts of multi- modal volumetric ima...
International audienceThis year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual...