Akbar, Shazia; Jordan, Lee B; Purdie, Colin A; Thompson, Alastair M; McKenna, Stephen J Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays Journal Article British Journal of Cancer, 113 , pp. 1075-1080, 2015. @article{Akbar2015a, title = {Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays}, author = {Shazia Akbar and Lee B. Jordan and Colin A. Purdie and Alastair M. Thompson and Stephen J. McKenna }, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4651129/}, year = {2015}, date = {2015-01-01}, journal = {British Journal of Cancer}, volume = {113}, pages = {1075-1080}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Akbar, Shazia; Jordan, Lee B; Thompson, Alastair M; McKenna, Stephen J Tumor localization in tissue microarrays using rotation invariant superpixel pyramids Conference IEEE International Symposium on Biomedical Imaging (ISBI), 2015. @conference{Akbar2015b, title = {Tumor localization in tissue microarrays using rotation invariant superpixel pyramids}, author = {Shazia Akbar and Lee B. Jordan and Alastair M. Thompson and Stephen J. McKenna }, url = {https://ieeexplore.ieee.org/abstract/document/7164111 https://www.shaziaakbar.co.uk/wp-content/uploads/2019/09/Akbar2015.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)}, abstract = {Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions. A superpixel representation retains information about visual structures such as cellular compartments, connective tissue, lumen and fatty tissue without having to commit to semantic segmentation at this level. In order to localize tumor in large images, a rotation invariant spatial pyramid representation is proposed using bags-of-superpixels. The method is evaluated on expert-annotated oestrogen-receptor stained TMA spots and compared to other superpixel classification techniques. Results demonstrate that it performs favorably.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions. A superpixel representation retains information about visual structures such as cellular compartments, connective tissue, lumen and fatty tissue without having to commit to semantic segmentation at this level. In order to localize tumor in large images, a rotation invariant spatial pyramid representation is proposed using bags-of-superpixels. The method is evaluated on expert-annotated oestrogen-receptor stained TMA spots and compared to other superpixel classification techniques. Results demonstrate that it performs favorably. |
Akbar, Shazia Tumour localisation in histopathology images PhD Thesis University of Dundee, 2015. @phdthesis{Akbar2015c, title = {Tumour localisation in histopathology images}, author = {Shazia Akbar}, url = {https://www.shaziaakbar.co.uk/wp-content/uploads/2019/09/Thesis.pdf}, year = {2015}, date = {2015-01-01}, school = {University of Dundee}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } |
Manivannan, Siyamalan; Li, Wenqi; Akbar, Shazia; Wang, Ruixuan; Zhang, Jianguo; McKenna, Stephen J HEp-2 cell classification using multi-resolution local patterns and ensemble SVMs Conference I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014, 2014. @conference{Manivannan2014a, title = {HEp-2 cell classification using multi-resolution local patterns and ensemble SVMs}, author = {Siyamalan Manivannan and Wenqi Li and Shazia Akbar and Ruixuan Wang and Jianguo Zhang and Stephen J. McKenna }, url = {https://ieeexplore.ieee.org/document/6973545}, year = {2014}, date = {2014-01-01}, booktitle = {I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014}, abstract = {We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%. |
Manivannan, Siyamalan; Li, Wenqi; Akbar, Shazia; Wang, Ruixuan; Zhang, Jianguo; McKenna, Stephen J HEp-2 specimen classification using multi-resolution local patterns and SVM Conference I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014, 2014. @conference{Manivannan2014b, title = {HEp-2 specimen classification using multi-resolution local patterns and SVM}, author = {Siyamalan Manivannan and Wenqi Li and Shazia Akbar and Ruixuan Wang and Jianguo Zhang and Stephen J. McKenna}, url = {https://ieeexplore.ieee.org/document/6973546}, year = {2014}, date = {2014-01-01}, booktitle = { I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014}, abstract = {A pattern recognition system was developed to classify immunofluorescence images of HEp-2 specimens into seven classes: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. Root-SIFT features together with multi-resolution local patterns were used to capture local shape and texture information. Sparse coding with max-pooling was applied to get an image representation from these local features. Specimens were classified using a linear support vector machine. Leave-one-specimen-out experiments on the I3A Contest Task 2 data set predicted a mean class accuracy of 89.9%.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } A pattern recognition system was developed to classify immunofluorescence images of HEp-2 specimens into seven classes: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. Root-SIFT features together with multi-resolution local patterns were used to capture local shape and texture information. Sparse coding with max-pooling was applied to get an image representation from these local features. Specimens were classified using a linear support vector machine. Leave-one-specimen-out experiments on the I3A Contest Task 2 data set predicted a mean class accuracy of 89.9%. |
McKenna, Stephen J; Amaral, Telmo; Akbar, Shazia; Jordan, Lee B; Thompson, Alastair M Immunohistochemical analysis of breast tissue microarray images using contextual classifiers Journal Article Journal of Pathology Informatics, 4 , 2013. @article{McKenna2013, title = {Immunohistochemical analysis of breast tissue microarray images using contextual classifiers}, author = {Stephen J. McKenna and Telmo Amaral and Shazia Akbar and Lee B. Jordan and Alastair M. Thompson}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3678746/}, year = {2013}, date = {2013-01-01}, journal = {Journal of Pathology Informatics}, volume = {4}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Akbar, Shazia; McKenna, Stephen J; Amaral, Telmo; Jordan, Lee B; Thompson, Alastair M Spin-context segmentation of breast tissue microarray images Journal Article Annals of BMVA, 4 , 2013. @article{Akbar2013, title = {Spin-context segmentation of breast tissue microarray images}, author = {Shazia Akbar and Stephen J. McKenna and Telmo Amaral and Lee B. Jordan and Alastair M. Thompson}, url = {https://www.shaziaakbar.co.uk/wp-content/uploads/2019/09/2013-0004.pdf}, year = {2013}, date = {2013-01-01}, journal = {Annals of BMVA}, volume = {4}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Akbar, Shazia; Amaral, Telmo; McKenna, Stephen J; Thompson, Alastair M; Jordan, Lee B Tumour segmentation in breast tissue microarray images using spin-context Conference Proceedings of Medical Image Understanding and Analysis (MIUA), 2012. @conference{Akbar2012, title = {Tumour segmentation in breast tissue microarray images using spin-context}, author = {Shazia Akbar and Telmo Amaral and Stephen J. McKenna and Alastair M. Thompson and Lee B. Jordan}, url = {https://www.shaziaakbar.co.uk/wp-content/uploads/2019/09/CS02.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {Proceedings of Medical Image Understanding and Analysis (MIUA)}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
McKenna, Stephen J; Amaral, Telmo; Akbar, Shazia; Thompson, Alastair M; Jordan, Lee B Immunohistochemical analysis of breast tissue microarray images using contextual classifiers Workshop Histopathology Image Analysis Workshop, MICCAI 2012, 2012. @workshop{McKenna2012, title = {Immunohistochemical analysis of breast tissue microarray images using contextual classifiers}, author = {Stephen J. McKenna and Telmo Amaral and Shazia Akbar and Alastair M. Thompson and Lee B. Jordan}, year = {2012}, date = {2012-01-01}, booktitle = {Histopathology Image Analysis Workshop, MICCAI 2012}, keywords = {}, pubstate = {published}, tppubtype = {workshop} } |
Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays Journal Article British Journal of Cancer, 113 , pp. 1075-1080, 2015. |
Tumor localization in tissue microarrays using rotation invariant superpixel pyramids Conference IEEE International Symposium on Biomedical Imaging (ISBI), 2015. |
Tumour localisation in histopathology images PhD Thesis University of Dundee, 2015. |
HEp-2 cell classification using multi-resolution local patterns and ensemble SVMs Conference I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014, 2014. |
HEp-2 specimen classification using multi-resolution local patterns and SVM Conference I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR 2014, 2014. |
Immunohistochemical analysis of breast tissue microarray images using contextual classifiers Journal Article Journal of Pathology Informatics, 4 , 2013. |
Spin-context segmentation of breast tissue microarray images Journal Article Annals of BMVA, 4 , 2013. |
Tumour segmentation in breast tissue microarray images using spin-context Conference Proceedings of Medical Image Understanding and Analysis (MIUA), 2012. |
Immunohistochemical analysis of breast tissue microarray images using contextual classifiers Workshop Histopathology Image Analysis Workshop, MICCAI 2012, 2012. |