Azam Hamidinekoo

Vita

09/2019 - Present
Post-Doctoral Training Fellow, Division of Molecular Pathology, the Institute of Cancer Research, London, UK
02/2019 - 07/2019
Research Scientist, Department of Computer Science, Aberystwyth University, United Kingdom
09/2015 - 09/2019
Ph.D., Department of Computer Science, Aberystwyth University, United Kingdom
09/2011 - 12/2013
M.Sc., Department of Electrical Engineering, Sharif University of Technology, Iran
09/2005 - 09/2009
B.Sc., Department of Biomedical Engineering, Amir-kabir University of Technology, Iran

Publications

Journal papers

  1. Hamidinekoo A., Garzòn-Martínez G. A., Ghahremani M., Jay-Fiona L., Zwiggelaar R., Doonan D. J., Lu C., DeepPod: A Convolutional Neural Network Based Quantification of Fruit Number in Arabidopsis, GigaScience, 2019.
  2. Suhail Z.,Hamidinekoo A., Zwiggelaar R., Mammographic Mass Classification Using Filter Response Patches, Journal of IET Computer Vision, 2018.
  3. Hamidinekoo A., Denton E., Rampun A., Honnor K., Zwiggelaar R., Deep Learning in Mammography and Breast Histology, an Overview and Future Trends, Journal of Medical Image Analysis, 2018.

Conference papers

  1. Hamidinekoo A., Afzali M., Zwiggelaar R., Povina F. V., Akanyeti O. , Automatic lesion detection and segmentation in sub-acute stroke survivors using artificial intelligence, The 18th Welsh Stroke Conference, Cardiff , UK, 2019.
  2. Hamidinekoo A., Denton E., Zwiggelaar R., Automated Mass Detection and Classification on Mammograms with a Deep Pipeline, International Conference on Medical Imaging with Deep Learning (MIDL), London, UK, 2019.
  3. Grall A., Hamidinekoo A., Zwiggelaar R., Using a generative adversarial network for prostate segmentation, Medical Image Understanding and Analysis (MIUA), Liverpool, UK, 2019.
  4. Hamidinekoo A., Chelly D. Z., Suhail Z., Zwiggelaar R., Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification, International Conference on Big Data: 1st Special Session on Health Care Data, Seattle, USA, 2018.
  5. Hamidinekoo A., Suhail Z., Denton E., Zwiggelaar R., Comparing the performance of various deep networks forbinary classification of breast tumours, the 14th International Workshop on Breast Imaging (IWBI’18), Georgia, USA, 2018.
  6. Suhail Z., Hamidinekoo A., Zwiggelaar R., Mammographic Mass Classification Using Filter Response Patches,BMVA Symposium: Computer Vision and Modelling in Cancer, London, UK, 2017.
  7. Hamidinekoo A., Zwiggelaar R., Stain Colour Normalisation to Improve Mitosis Detection on Breast HistologyImages, the 7th MICCAI workshop on Deep Learning in Medical Image Analysis (DLMIA), Quebec City, Canada, 2017.
  8. Hamidinekoo A., Suhail Z., Qaiser T., Zwiggelaar R., Investigating the effect of various augmentations on the input data fed to a Convolutional Neural Network for the task of mammographic mass classification, Medical Image Understanding and Analysis (MIUA), Edinbrough, UK, 2017.
  9. Suhail Z., Hamidinekoo A., Denton E., Zwiggelaar R., A Texton-Based Approach for the Classification of Benign and Malignant Masses in Mammograms, Medical Image Understanding and Analysis (MIUA), Edinbrough, UK, 2017.
  10. Hamidinekoo A., Denton E., Zwiggelaar R., Breast Cancer Prediction and Phenotyping based on Both Mammographic and Histologic Data, 11th Workshop for Women in Machine Learning (WiML 2016) Co-located with NIPS, Barcelona, Spain, 2016.
  11. Babolhavaeji A., Karimi S., Ghaffari A. Hamidinekoo A., Vosoughi Vahdat B., Optimal Temporal Resolution For Decoding Of Visual Stimuli In Inferior Temporal Corte (ICBME), Tehran, Iran, 2014.
  12. Azampour M.F., Ghaffari A., Hamidinekoo A., Fatemizadeh E., Manifold Learning Based Registration Algorithms Applied to Multimodal Images, IEEE EMBC’14, Chicago, USA, 2014.
  13. Hamidinekoo A., Fatemizadeh E., Nonrigid Registration of Breast MR Images Using Intensity-Unbiased Force in Variational Motion Estimation, IEEE EMBS ISC, Cairo, Egypt, 2013.
  14. Hamidinekoo A., Ghaffari A., Fatemizadeh E., Registration of Breast MR Images Using Residual Complexity Similarity Measure, MVIP, Zanjan, Iran, 2013.

Award and Scholarships

  1. First place for the presented projects in the 18th Welsh Stroke Conference: Dragons Den, Cardiff Metropolitan University, Cardiff, UK, 2019.
  2. Second place for the poster presentation in the 18th Welsh Stroke Conference, Cardiff Metropolitan University, Cardiff, UK, 2019.
  3. Accepted for the Graduate School Funding for Student-led Initiatives: Postgraduate Community Competition, Aberystwyth University, UK, 2019.
  4. Second place in the Third Year Postgraduate Oral Presentation, Aberystwyth University, UK, 2018.
  5. First place in the Second Year Postgraduate Poster Presentation, Aberystwyth University, UK, 2017.
  6. Second place in the Postgraduate Three Minute Thesis Presentation (2016-17), Aberystwyth University, UK, 2017.
  7. Third place in the 13th London Hopper Colloquium, postgraduate students spotlight competition, BCS Academy and University College London, UK, 2017.
  8. Doctoral Career Development Scholarship (DCDS), Aberystwyth University, UK, 2015.
  9. Computer Science Department Overseas PhD Scholarship (CSDOPS), Aberystwyth University, UK, 2015.
  10. Travel award by IEEE-EMBC to attend the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14), Chicago, USA, 2014.
  11. Travel award by International Research-Centered Summer School at National Center for Scientific Research (NCSR), Demokritos, Athens, Greece, 2014.
  12. First paper award by the 1st IEEE EMBS International Student Conference, Cairo, Egypt, 2013.
  13. Travel award by the 1st IEEE EMBS International Summer School on Neural Engineering (ISSNE),Shanghai, China, 2013.