Skip to main content Skip to navigation



  • R. Verma, N. Kumar, A. Patil, N. C. Kurian, S. Rane, S. Graham, Q. Dang Vu, M. Zwager, S. E. A. Raza, N. Rajpoot, X. Wu, H. Chen, Y. Huang, L. Wang, H. Jung, G. T. Brown, Y. Liu, S. Liu, S. A. F. Jahromi, A. A. Khani, E. Montahaei, M. S. Baghshah, H. Behroozi, P. Semkin, A. Rassadin, P. Dutande, R. Lodaya, U. Baid, B. Baheti, S. Talbar, A. Mahbod, R. Ecker, I. Ellinger, Z. Luo, B. Dong, Z. Xu, Y. Yao, S Lv, M. Feng, K. Xu, H. Zunair, A. B. Hamza, S. Smiley, T.-K. Yin, Q. Fang, S. Srivastava, D. Mahapatra, L. Trnavska, H. Zhang, P. L. Narayanan, J. Law, Y. Yuan, A. Tejomay, A. Mitkari, D. Koka, V. Ramachandra, L. Kini & A. Sethi, “MoNuSAC2020: A Multi-organ Nuclei Segmentation and Classification Challenge” IEEE Transactions on Medical Imaging, June 2021. [Abstract] [doi]
  • Q. Dang Vu, C. Fong, K. von Loga, S. E. A. Raza, D. N. Rodrigues, B. Patel, C. Peckitt, R. Begum, A. Athauda, N. Starling, I. Chau, S. Rao, D. J. Watkins, M. Rebelatto, T. Waddell, J. Wadsley, T. Roques, M. Hewish, D. Cunningham & N. M. Rajpoot, “Digital histological markers based on routine H\&E slides to predict benefit from maintenance immunotherapy in esophagogastric adenocarcinoma.” Journal of Clinical Oncology, 39(15\_suppl), 2021, e16074-e16074. [Abstract] [doi]
  • P. L. Narayanan, S. E. A. Raza, A. H. Hall, J. R. Marks, L. King, R. B. West, L. Hernandez, N. Guppy, M. Dowsett, B. Gusterson, C. Maley, E. S. Hwang & Y. Yuan, “Unmasking the immune microecology of ductal carcinoma in situ with deep learning,” NPJ Breast Cancer, 7(19), Mar 2021. [Abstract] [doi]
  • J. M. Winfield, J. C. Wakefield, J. D. Brenton, K. AbdulJabbar, A. Savio, S. Freeman, E. Pace, K. Lutchman-Singh, K. M. Vroobel, Y. Yuan, S. Banerjee, N. Porta, S. E. A. Raza & N. M. deSouza, “Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis,” British Journal of Cancer, Jan 2021. [Abstract] [doi]


  • A. Pennycuick, V. H Teixeira, K. AbdulJabbar, S. E. A. Raza, T. Lund, A. U. Akarca, R. Rosenthal, L. Kalinke, D. P. Chandrasekharan, C. P. Pipinikas, H. Lee-Six, R. E. Hynds, K. H.C. Gowers, J. Y Henry, F. R. Millar, Y. B Hagos, C. Denais, M. Falzon, D. A Moore, S. Antoniou, P. F. Durrenberger, A. J. S. Furness, B. Carroll, C. Marceaux, M.e Asselin-Labat, W. Larson, C. Betts, L. M. Coussens, R. M. Thakrar, J. George, C. Swanton, C. Thirlwell, P. J. Campbell, T. Marafioti, Y. Yuan, S. A. Quezada, N. McGranahan, S. M. Janes, “Immune surveillance in clinical regression of pre-invasive squamous cell lung cancer,” Cancer Discovery, July 2020. [Abstract] [doi]
  • K. AbdulJabbar*, S. E. A. Raza*, R. Rosenthal, M. Jamal-Hanjani, S. Veeriah, A. Akarca, T. Lund, D. Moore, R. Salgado, M. Al Bakir, L. Zapata, C. Hiley, L. Officer, M. Sereno, C. Smith, S. Loi, A. Hackshaw, T. Marafioti, S. Quezada, N. McGranahan, J. Le Quesne, C. Swanton & Y. Yuan, “Geospatial immune variability illuminates differential evolution of lung adenocarcinoma,” Nature Medicine, May 2020, p. 1-9. [Abstract] [doi]
  • R. M. S. Bashir, T. Qaiser, S. E. A. Raza, & N. M. Rajpoot, “HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification,” in Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC 2020, MIL3ID 2020. [Abstract] [doi]
  • A. Yaar, A. Asif, S. E. A. Raza, N.M. Rajpoot & F. Minhas, “Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2020 USA. [Abstract] [doi]
  • R. M. S. Bashir, H. Mahmood, M. Shaban, S. E. A. Raza, M. M. Fraz, S. A. Khurram & N. M. Rajpoot, “Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images,” in Medical Imaging 2020: Digital Pathology, Houston, Texas, USA, vol. 11320, p. 1132011. [Abstract] [doi]


  • S. Graham, Q. Dang, S. E. A. Raza, J.T. Kwak, N.M. Rajpoot, “Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis, Dec. 2019, vol. 58, p. 101563. [Abstract] [doi] [Data]
  • H. M. Alghamdi, M. Althobiti, T. Qaiser, A. R. Green, S. E. A. Raza, E. A. Rakha & N. M. Rajpoot, “A Hybrid Pipeline to Assess Oestrogen Receptor Stained Nuclei in Invasive Breast Cancer,” in COMPAY 2019: MICCAI, Shenzhen, China [Abstract] [doi]
  • K. Zormpas-Petridis, H. Failmezger, S. E. A. Raza, et al., “Superpixel-based Conditional Random Fields (SuperCRF): Incorporating global and local context for enhanced deep learning in melanoma histopathology,” Frontiers in Oncology, Sep. 2019. [Abstract] [doi]
  • S. E. A. Raza, K. AbdulJabbar, M. Jamal-Hanjani, et al., “Deconvolving convolution neural network for cell detection,” IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 2019, p. 891–894. [Abstract] [doi]


  • S. E. A. Raza, L. Cheung, M. Shaban, et al., “Micro-Net: A unified model for segmentation of various objects in microscopy images,” Medical Image Analysis, Dec. 2018, vol. 52, p. 160–173. [Abstract] [doi] [Data]
  • P. L. Narayanan, S. E. A. Raza, A. Dodson, et al., “DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images,” in Medical Imaging with Deep Learning (MIDL) , 2018 [Abstract] [doi]
  • N. Alsubaie, K. Sirinukunwattana, S. E. A. Raza, et al., “A bottom-up approach for tumour differentiation in whole slide images of lung adenocarcinoma,” in Medical Imaging : Digital Pathology, Mar. 2018, pp. 105810E, vol. 10581. [Abstract] [doi]


  • S. E. A. Raza, L. Cheung, D. Epstein, et al., “Mimonet: Gland segmentation using multi-input-multi-output convolutional neural network,” In Medical Image Understanding and Analysis (MIUA), Jul. 2017, pp. 698–706. [Abstract] [doi]
  • S. E. A. Raza, L. Cheung, D. Epstein, et al., “MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images,” IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 2017, p. 337-340. [Abstract] [doi]
  • G. Li, S.E.A. Raza, N.M. Rajpoot, “Multi-Resolution Cell Orientation Congruence Descriptors for Epithelium Segmentation in Endometrial Histology Images,” Medical Image Analysis, Jan. 2017, vol. 37, p. 91–100. [Abstract] [doi]
  • N. Alsubaie, N. Trahearn, S.E.A. Raza et al., “Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation,” PLoS One, Jan. 2017, vol. 12, no. 1, p.e0169875. [Abstract] [doi]


  • N. Alsubaie, S. E. A. Raza, and N. M. Rajpoot, “Stain Deconvoloution of Histology Images via Independent Component Analysis in the Wavelet Domain,” IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 2016, p. 803-806. [Abstract] [doi]
  • M.N. Kashif, S.E.A. Raza, K. Sirinukunwattana, et al., “Handcrafted features with convolutional neural networks for detection of tumor cells in histology images,” IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 2016, p. 1029-1032. [Abstract] [doi]
  • S. E. A. Raza, D. Langenkämper, K. Sirinukunwattana, D. B. A. Epstein, T. W. Nattkemper, and N. M. Rajpoot, “Robust Normalization Protocols for Multiplexed Fluorescence Bioimage Analysis,” BMC Biodata Min., Mar. 2016, vol. 9:11. [Abstract] [doi]
  • K. Sirinukunwattana, S.E.A. Raza, Y.-W. Tsang, D. Snead, I. Cree, and N.M. Rajpoot, “Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images,” IEEE Trans. Med. Imaging, pp. 1–1, Jan. 2016. [Abstract] [doi] [Data]


  • K. Sirinukunwattana, S. E. A.Raza, Y.-W. Tsang, D. Snead, I. Cree, and N.M. Rajpoot, “A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images,” in 1st International Workshop on Patch-based Techniques in Medical Imaging, MICCAI, Oct. 2015, pp. 154–162. [Abstract] [doi]
  • G. Li, S. E. A. Raza, and N.M. Rajpoot, “A Novel Cell Orientation Congruence Descriptor for Superpixel Based Epithelium Segmentation in Endometrial Histology Images,” in 1st International Workshop on Patch-based Techniques in Medical Imaging, MICCAI, Oct. 2015, pp. 172–179. [Abstract] [doi]
  • S.E.A. Raza, V. Sanchez, G. Prince, J. Clarkson, and N. M. Rajpoot, “Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain,” Pattern Recognit., vol. 48, pp. 2119–2128, Jul. 2015. [Abstract] [doi][Software]
  • S. E. A. Raza and N. M. Rajpoot, “Cell Nuclei Segmentation in Variable Intensity Fluorescence Microscopy Images,” in Medical Image Understanding and Analysis, Jul. 2015, pp. 28–33. [Abstract] [doi]
  • N. Alsubaie, N. Trahearn, S. E. A. Raza, and N. M. Rajpoot, “A Discriminative Framework for Stain Deconvolution of Histopathology Images in the Maxwellian Space,” in Medical Image Understanding and Analysis, Jul. 2015, pp. 132–137. [Abstract] [doi]
  • S. E. A. Raza, G. Prince, J. Clarkson, and N. M. Rajpoot, “Automatic Detection of Diseased Tomato Plants using Thermal and Stereo Visible Light Images,” PLoS One, Apr. 2015. [Abstract] [doi]
  • S. E. A. Raza, M. Q. Marjan, M. Arif, F. Butt, F. Sultan, and N. M. Rajpoot, “Anisotropic tubular filtering for automatic detection of acid-fast bacilli in Ziehl-Neelsen stained sputum smear samples,” in SPIE Medical Imaging, Feb. 2015, vol. 9420, p. 942005. [Abstract] [doi]


  • A.M. Khan, S.E.A. Raza, M. Khan, et al., “Cell Phenotyping in Multi-Tag Fluorescent Bioimages,” Neurocomputing, Jun. 2014, vol. 134 no. 1 p. 254-261. [Abstract] [doi]
  • S.E.A Raza, H. Smith, G.J.J. Clarkson, et al., “Automatic Detection of Regions in Spinach Canopies Responding to Soil Moisture Deficit Using Combined Visible and Thermal Imagery,” PLoS ONE, Jun. 2014, vol. 9 no. 6 p. e97612. [Abstract] [doi]


  • S.E.A. Raza, A. Humayun, S. Abouna, et al., “RAMTaB: Robust Alignment of Multi-Tag Bioimages,” PLoS ONE, Feb. 2012, vol. 7 no. 2, p. e30894. [Abstract] [doi][Software]
  • A. M. Khan, A. Humayun, S. E. A. Raza, et al., “A Novel Paradigm for Mining Cell Phenotypes in Multi-tag Bioimages Using a Locality Preserving Nonlinear Embedding,” Proceedings Neural Information Processing. ICONIP, Lecture Notes in Computer Science , vol. 7666, 2012. [Abstract] [doi]


  • A. Humayun, S.E.A. Raza, C. Waddington, et al., “A Framework for Molecular Co-Expression Pattern Analysis in Multi-Channel Toponome Fluorescence Images,” Proceedings Microscopy Image Analysis with Applications in Biology (MIAAB), Sep. 2011, Heidelberg, Germany. [Abstract] [doi]