I have completed my Ph.D. study under the supervision of Prof. Nasir M. Rajpoot in May 2016, and is now a post-doctoral research fellow at Beck Lab, BIDMC, Harvard Medical School. I am interested in statistical machine learning methods and their applications in biology, especially in cancer research.
The main objective of my Ph.D. research is to identify a combination of protein markers that can predict outcomes of colorectal cancer, diagnosed as TNM-stage II, more accurately than TNM stage or other standard pathological stages alone. The analysis is largely relying on colon histology images, and clinical, proteomic (immunohistochemistry), and genomic profiles. To this end, I am focusing on developing image analysis and statistical machine learning techniques to rigorously analyse the data. The success in identifying more accurate prognostic markers will help with early prognosis, disease monitoring, and development of a new therapy.
You can find my research projects here. [research page]
2013 - present PhD candidate, Department of Computer Science, The University of Warwick, UK
2010-2012 M.Sc. Complex Systems Science (Erasmus Mundus)
2006-2010 B.Sc. Mathematics, Mahidol University, Thailand
You can find my CV here. [pdf]
- K. Sirinukunwattana, S. E. A. Raza, Y. Tsang, I. A. Cree, D. R. J. Snead, N. M. Rajpoot. "Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images", IEEE Transcactions on Medical Imaging (2015); (in press) [pdf]
- A. M. Khan, K. Sirinukunwattana, and N. M. Rajpoot. "A global covariance descriptor for nuclear atypia scoring in breast histopathology images", IEEE Journal of Biomedical and Health Informatics (2015); http://dx.doi.org/10.1109/JBHI.2015.2447008
- K. Sirinukunwattana, D .R. J. Snead, N. M. Rajpoot. "A Stochastic Polygons Model for Glandular Structures in Colon Histology Images", IEEE Transactions on Medical Imaging (2015); http://dx.doi.org/10.1109/TMI.2015.2433900.
- K. Sirinukunwattana, A. M. Khan, and N. M. Rajpoot. "Cell words: Modelling the visual appearance of cells in histopathology images", Computerized Medical Imaging and Graphics (2014); http://dx.doi.org/10.1016/j.compmedimag.2014.11.008.
- K. Sirinukunwattana, R. S. Savage, M. F. Bari, D. R. J. Snead, N. M. Rajpoot. "Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data with Unknown Statistics", PLOS ONE (2013); http://dx.doi.org/10.1371/journal.pone.0075748.
- K. Sirinukunwattana, Y. Lenbury, N. Tumrasvin. "Drug Resistant and Wild-type Strains Interaction: Investigating Effects of Conversion Delays for Possible Control Strategies", International Journal of Mathematical Models and Methods in Applied Sciences 4(5), pp. 830-838 (2011). [pdf]
- K. Sirinukunwattana, S. E. A. Raza, Y. Tsang, D. R. J. Snead, I. A. Cree, and N. M. Rajpoot. "A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images" International Workshop on Patch-based Techniques in Medical Imaging (Patch-MI) (2015).
- K. Sirinukunwattana, D. R. J. Snead, N. M. Rajpoot. "A Random Polygons Model of Glandular Structures in Colon Histology Images", International Symposium on Biomedical Imaging (ISBI) (2015); [preprint].
- K. Sirinukunwattana, D. R. J. Snead, N. M. Rajpoot. "A Novel Texture Descriptor for Detection of Glandular Structures in Colon Histology Images", Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology (2015); http://dx.doi.org/10.1117/12.2082010.
- A. M. Khan, K. Sirinukunwattana, N. M. Rajpoot. "Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images", In 5th International Workshop in Machine Learning and Medical Imaging (MLMI) (2014); http://dx.doi.org/10.1007/978-3-319-10581-9_13.
- N. A. Aloraidi, K. Sirinukunwattana, A. M. Khan, and N. M. Rajpoot. "On generating cell exemplars for detection of mitotic cells in breast cancer histopathology images", In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3370-3373; http://dx.doi.org/10.1109/EMBC.2014.6944345.
- V. N. Kovacheva, K. Sirinukunwattana, N. M. Rajpoot. "A Bayesian Framework for Cell-Level Protein Network Analysis for Multivariate Proteomics Image Data", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 904110 (March 20, 2014); http://dx.doi.org/10.1117/12.2045028.
Conference Presentations (without Publication)
- European Congress on Digital Pathology, 18-21 June 2014, Collège des Bernardins, Paris. Oral presentation "Cell Words : Modelling the Visual Appearance in Histopathology Images"
- Qatar Foundation Annual Research Conference, 18-19 November 2014. Poster presentation "A Novel Approach To Detection Of Glandular Structures In Colorectal Cancer Histology Images"
- Digital Pathology Congress, London, 4-5 December 2014. Poster presentation "A Novel Approach to Segmentation of Glandular Structures in Colorectal Adenocarcinoma Histology Images"
Qatar National Research Fund (QNRF) grant no. NPRP5-1345-1-228 and the Department of Computer Science, University of Warwick.
K dot Sirinukunwattana at warwick dot ac dot uk
- 2015-10-09: Our paper 'A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Histology Images' was awarded best paper at Patch-MI'2015 workshop [link]
- 2015-05-13. Our paper entitled 'A Stochastic Polygons Model for Glandular Structures in Colon Histology Images' is accepted for publication in IEEE Transactions on Medical Imaging
2015-01-30. We are organising GlaS: Gland Segmentation in Colon Histology Images Challenge in MICCAI 2015
- 2014-08-24. Our breast nuclear atypia grading algorithm listed the first in the Mitos-Atypia 14 Contest [link]