Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features

Doyle, Scott; Agner, Shannon C.; Madabhushi, Anant; Feldman, Michael D.; Tomaszewski, John E.

doi:10.1109/isbi.2008.4541041

Cited by 208 publications

(188 citation statements)

References 9 publications

Supporting

Mentioning

186

Contrasting

Order By: Relevance

“…A variety of prior works have used computational feature extraction from images for classification, but many of these methods selected smaller areas of the tumor for processing (Alexe et al, 2009;Adiga et al, 2006;Aiad et al, 2009;Al-Kadi, 2010;Altunbay et al, 2010;Baak et al, 1981;Basavanhally et al, 2010Basavanhally et al, , 2008Brook et al, 2007;Doyle et al, 2012aDoyle et al, , 2012bDoyle et al, , 2008Doyle et al, , 2007; There were no inaccurate predictions for any alpha value at lambda min. Accuracy of the model is stable over much of the parameter range, with lower accuracy only occurring where lambda forces few to no features to be included in the model.…”

Section: Resultsmentioning

confidence: 99%

“…A similar approach is to manually designate a region in the WSI for analysis (Al-Kadi, 2010;Basavanhally et al, 2010Basavanhally et al, , 2008Doyle et al, 2012aDoyle et al, , 2008Doyle et al, , 2007Dundar et al, 2011Dundar et al, , 2010Qureshi et al, 2008;Sertel et al, 2010). This is the digital equivalent of the subsetting process described above: in both procedures, a human uses their knowledge and time to reduce the data available for analysis based on their evaluation of what is informative.…”

Section: Image Subsetting Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

View full text Add to dashboard Cite

a b s t r a c tComputerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1 % (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Image Subsetting Methodsmentioning

confidence: 99%

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

View full text Add to dashboard Cite

show abstract

“…Doyle et al [22] apply textural and graphical feature assignment techniques (e.g. Voronoi tessellation, Delaunay triangulation, and minimum spanning tree, etc.)…”

Section: Cluster Featuresmentioning

confidence: 99%

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides

Saribudak¹,

Dong²,

Hsieh³

et al. 2016

IJET

View full text Add to dashboard Cite

“…Petushi et al [4,5] introduced a system able to label several histological and cytological microstructures in high resolution frames, including different grades of epithelial cells, fat cells and stroma. Doyle et al [6,7] proposed a method based on geometrical features, to distinguish between healthy tissue, low grade and high grade cancer. Tutac et al [8] initiated an innovative knowledge guided approach relying on the prior modeling of medical knowledge using ontology designed according to the clinical standard called Nottingham Grading System [9].…”

Section: Introductionmentioning

confidence: 99%

Time-efficient sparse analysis of histopathological whole slide images

Huang

Veillard

Roux

et al. 2011

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

Histopathological examination is a powerful method 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 specific high and generic 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. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing 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 (40X) high power fields. The time processing 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.

show abstract

Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features

Cited by 208 publications

References 9 publications

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides

Time-efficient sparse analysis of histopathological whole slide images

Contact Info

Product

Resources

About