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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Pages | 2203-2210 |
| Number of pages | 8 |
| DOIs | |
| State | Published - 2013 |
| Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: Jun 23 2013 → Jun 28 2013 |
Other
| Other | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 |
|---|---|
| Country | United States |
| City | Portland, OR |
| Period | 6/23/13 → 6/28/13 |
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ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
Cite this
Classification of tumor histology via morphometric context. / Chang, Hang; Borowsky, Alexander; Spellman, Paul; Parvin, Bahram.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013. p. 2203-2210 6619130.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Classification of tumor histology via morphometric context
AU - Chang, Hang
AU - Borowsky, Alexander
AU - Spellman, Paul
AU - Parvin, Bahram
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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U2 - 10.1109/CVPR.2013.286
DO - 10.1109/CVPR.2013.286
M3 - Conference contribution
AN - SCOPUS:84887390740
SP - 2203
EP - 2210
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ER -