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All Outputs (7)

Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation (2019)
Book Chapter
Hou, X., Liu, J., Xu, B., Liu, B., Chen, X., Garibaldi, J., …Qiu, G. (2019). Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II (101-109). Springer Verlag. https://doi.org/10.1007/978-3-030-32245-8_12

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtainin... Read More about Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification (2015)
Journal Article
Aleskandarany, M. A., Soria, D., Green, A. R., Nolan, C., Diez-Rodriguez, M., Ellis, I. O., & Rakha, E. A. (2015). Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification. Breast Cancer Research and Treatment, 151(2), 325-333. https://doi.org/10.1007/s10549-015-3406-3

Accurate distant metastasis (DM) prediction is critical for risk stratification and effective treatment decisions in breast cancer (BC). Many prognostic markers/models based on tissue marker studies are continually emerging using conventional statist... Read More about Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification.

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer (2014)
Journal Article
Rakha, E., Soria, D., Green, A. R., Lemetre, C., Powe, D. G., Nolan, C. C., …Ellis, I. O. (2014). Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer. British Journal of Cancer, 110(7), https://doi.org/10.1038/bjc.2014.120

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. I... Read More about Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer.

A quantifier-based fuzzy classification system for breast cancer patients (2013)
Journal Article
Soria, D., Garibaldi, J. M., Green, A. R., Powe, D. G., Nolan, C. C., Lemetre, C., …Ellis, I. O. (2013). A quantifier-based fuzzy classification system for breast cancer patients. Artificial Intelligence in Medicine, 58(3), https://doi.org/10.1016/j.artmed.2013.04.006

Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms ha... Read More about A quantifier-based fuzzy classification system for breast cancer patients.

A "non-parametric" version of the naive Bayes classifier (2011)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A "non-parametric" version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), https://doi.org/10.1016/j.knosys.2011.02.014

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence... Read More about A "non-parametric" version of the naive Bayes classifier.

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients (2010)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., …Ellis, I. O. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine, 40(3), https://doi.org/10.1016/j.compbiomed.2010.01.003

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’... Read More about A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

A comparison of three different methods for classification of breast cancer data (2008)
Conference Proceeding
Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. (2008). A comparison of three different methods for classification of breast cancer data.

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classi... Read More about A comparison of three different methods for classification of breast cancer data.