An Exploration Scheme for Large Images: Application to Breast Cancer Grading

Antoine Veillard, Nicolas Loménie, Daniel Racoceanu

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7 Citas (Scopus)

Resumen

Most research works focus on pattern recognition within a small sample images but strategies for running efficiently these algorithms over large images are rarely if ever specifically considered. In particular, the new generation of satellite and microscopic images are acquired at a very high resolution and a very high daily rate. We propose an efficient, generic strategy to explore large images by combining computational geometry tools with a local signal measure of relevance in a dynamic sampling framework. An application to breast cancer grading from huge histopathological images illustrates the benefit of such a general strategy for new major applications in the field of microscopy.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Páginas3472-3475
Número de páginas4
DOI
EstadoPublicada - 2010
Publicado de forma externa
Evento2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turquía
Duración: 23 ago. 201026 ago. 2010

Serie de la publicación

NombreProceedings - International Conference on Pattern Recognition
ISSN (versión impresa)1051-4651

Conferencia

Conferencia2010 20th International Conference on Pattern Recognition, ICPR 2010
País/TerritorioTurquía
CiudadIstanbul
Período23/08/1026/08/10

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