Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compmedimag.2010.11.009
Title: Time-efficient sparse analysis of histopathological whole slide images
Authors: Huang, C.-H. 
Veillard, A.
Roux, L.
Loménie, N.
Racoceanu, D. 
Keywords: Breast cancer grading
Digitized histopathology
Dynamic sampling
Graphics processing unit
Multi-scale analysis
Virtual microscopy
Whole slide image
Issue Date: 2011
Source: Huang, C.-H., Veillard, A., Roux, L., Loménie, N., Racoceanu, D. (2011). Time-efficient sparse analysis of histopathological whole slide images. Computerized Medical Imaging and Graphics 35 (7-8) : 579-591. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compmedimag.2010.11.009
Abstract: Histopathological examination is a powerful standard for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of whole slide images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on application-driven for high-resolution and generic for low-resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. Sparse coding and dynamic sampling constitute the keystone of our approach. These methods are implemented within a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40×) high power fields. The processing time to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology. © 2010 Elsevier Ltd.
Source Title: Computerized Medical Imaging and Graphics
URI: http://scholarbank.nus.edu.sg/handle/10635/39094
ISSN: 08956111
DOI: 10.1016/j.compmedimag.2010.11.009
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