Name
Abstract | ||
|---|---|---|
Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types; (ii) robust in the presence of wide technical and biological variations; (iii) invariant to different nuclear segmentation strategies; and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.286 | CVPR |
Keywords | Field | DocType |
nuclear level morphometric feature,tumor composition,tissue histology,image-based classification,different tumor type,different nuclear segmentation strategy,biological variation,large cohort,different component,tumor histology,morphometric context,bioinformatics,biomedical research,image classification,kernel,image segmentation,dictionaries,histograms | Data mining,Pattern recognition,Segmentation,Computer science,Image matching,Image representation,Artificial intelligence,Invariant (mathematics),Pyramid,Contextual image classification,Histology,Sample size determination | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
17 | 0.84 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Chang | 1 | 374 | 29.11 |
| Alexander Borowsky | 2 | 80 | 5.39 |
| Paul Spellman | 3 | 453 | 43.25 |
| B. Parvin | 4 | 203 | 19.16 |