Abstract
Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful computer vision based feature descriptors for recognizing cancerous tissues in histopathologic images. To this end, we use images of Hematoxylin & Eosin-stained microscopic sections of breast and prostate carcinomas, and myometrial leiomyosarcomas, and provide an exhaustive evaluation of several state of the art feature representations for this task. Among the various image descriptors that we chose to compare, including representations based on convolutional neural networks, Fisher vectors, and sparse codes, we found that working with covariance based descriptors shows superior performance on all three types of cancer considered. While covariance descriptors are known to be effective for texture recognition, it is the first time that they are demonstrated to be useful for the proposed task and evaluated against deep learning models. Capitalizing on Region Covariance Descriptors (RCDs), we derive a powerful image descriptor for cancerous tissue recognition termed, Covariance Kernel Descriptor (CKD), which consistently outperformed all the considered image representations. Our experiments show that using CKD lead to 92.83%, 91.51%, and 98.10% classification accuracy for the recognition of breast carcinomas, prostate carcinomas, and myometrial leiomyosarcomas, respectively.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 23rd International Conference on Pattern Recognition, ICPR 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1490-1495 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509048472 |
| DOIs | |
| State | Published - Apr 13 2017 |
| Event | 23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: Dec 4 2016 → Dec 8 2016 |
Other
| Other | 23rd International Conference on Pattern Recognition, ICPR 2016 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 12/4/16 → 12/8/16 |
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Evaluation of feature descriptors for cancerous tissue recognition. / Stanitsas, Panagiotis; Cherian, Anoop; Li, Xinyan; Truskinovsky, Alexander; Morellas, Vassilios; Papanikolopoulos, Nikolaos P.
2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1490-1495 7899848.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of feature descriptors for cancerous tissue recognition
AU - Stanitsas, Panagiotis
AU - Cherian, Anoop
AU - Li, Xinyan
AU - Truskinovsky, Alexander
AU - Morellas, Vassilios
AU - Papanikolopoulos, Nikolaos P
PY - 2017/4/13
Y1 - 2017/4/13
N2 - Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful computer vision based feature descriptors for recognizing cancerous tissues in histopathologic images. To this end, we use images of Hematoxylin & Eosin-stained microscopic sections of breast and prostate carcinomas, and myometrial leiomyosarcomas, and provide an exhaustive evaluation of several state of the art feature representations for this task. Among the various image descriptors that we chose to compare, including representations based on convolutional neural networks, Fisher vectors, and sparse codes, we found that working with covariance based descriptors shows superior performance on all three types of cancer considered. While covariance descriptors are known to be effective for texture recognition, it is the first time that they are demonstrated to be useful for the proposed task and evaluated against deep learning models. Capitalizing on Region Covariance Descriptors (RCDs), we derive a powerful image descriptor for cancerous tissue recognition termed, Covariance Kernel Descriptor (CKD), which consistently outperformed all the considered image representations. Our experiments show that using CKD lead to 92.83%, 91.51%, and 98.10% classification accuracy for the recognition of breast carcinomas, prostate carcinomas, and myometrial leiomyosarcomas, respectively.
AB - Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful computer vision based feature descriptors for recognizing cancerous tissues in histopathologic images. To this end, we use images of Hematoxylin & Eosin-stained microscopic sections of breast and prostate carcinomas, and myometrial leiomyosarcomas, and provide an exhaustive evaluation of several state of the art feature representations for this task. Among the various image descriptors that we chose to compare, including representations based on convolutional neural networks, Fisher vectors, and sparse codes, we found that working with covariance based descriptors shows superior performance on all three types of cancer considered. While covariance descriptors are known to be effective for texture recognition, it is the first time that they are demonstrated to be useful for the proposed task and evaluated against deep learning models. Capitalizing on Region Covariance Descriptors (RCDs), we derive a powerful image descriptor for cancerous tissue recognition termed, Covariance Kernel Descriptor (CKD), which consistently outperformed all the considered image representations. Our experiments show that using CKD lead to 92.83%, 91.51%, and 98.10% classification accuracy for the recognition of breast carcinomas, prostate carcinomas, and myometrial leiomyosarcomas, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85019127456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019127456&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2016.7899848
DO - 10.1109/ICPR.2016.7899848
M3 - Conference contribution
SP - 1490
EP - 1495
BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -