Ylenia Giarratano

Thesis title: Developing new diabetic retinopathy biomarkers through image processing, computational modelling, and machine learning

Biography

Background

I am PhD candidate in Precision Medicine at the University of Edinburgh and I am passionate about Mathematics, Image Processing,  and Data Science. I studied Mathematics at the University of Palermo (IT) where I received my bachelor’s degree and I continued my studies with a master’s degree in Mathematics for Life Sciences at the University of Trento (IT).  After my graduation, I pursued my career as an intern at the University of Edinburgh in the School of Informatics with an Erasmus+ scholarship. In 2017, I have started my current PhD position.

Qualifications

-2017-present: PhD Candidate, Usher Institute, The University of Edinburgh (UK)

-2014-2016: MSc in  Mathematics for Life Sciences,  University of Trento (IT)

-2010-2014: Undergraduate Degree in Mathematics, University of Palermo (IT)

Undergraduate teaching

Course tutor: Quantitative Skills for Biologists 1 (2018-2019) (2019-2020)[SV1-SEM1]

Lab demonstrator: Molecules to Society 2a (2018-2019)(2019-2020)[MBChB]

Research summary

My research interests involve the application of image processing, statistics, graph theory, and machine learning methodologies to study structural and functional markers able to capture changes in the retinal vasculature due to diseases.

Current project grants

MRC DTP Precision Medicine MR/N013166/1

Conference details

SINAPSE 2019 Annual Scientific Meeting, oral presentation (Dundee, UK)

SINAPSE 2020 Annual Scientific Meeting (virtual meeting),

ARVO 2020 Annual Meeting (virtual meeting)

MICCAI Virtual Conference 2020

 

Organiser

MICCAI 2019 - MICCAI Student Board Member (Social-Officer)

MICCAI 2020- MICCAI Student Board Member (Social-Officer)

MICCAI 2021- MICCAI Student Board Member (Webinar-Officer)

Participant

MICCAI 2018 : International Conference on Medical Image Computing & Computer Assisted Intervention

SMB 2020 annual meeting of the society for mathematical biology (virtual meeting)

Papers delivered

OMIA 2020 (MICCAI2020 workshop) : 

  • Giarratano Y. et al. (2020) A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). In: Fu H., Garvin M.K., MacGillivray T., Xu Y., Zheng Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_16
  • Andreeva R., Fontanella A., Giarratano Y., Bernabeu M.O. (2020) DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis. In: Fu H., Garvin M.K., MacGillivray T., Xu Y., Zheng Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_2

  • Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello: Phylogenetic convolutional neural networks in metagenomics. BMC Bioinformatics 19-S(2): 49:1-49:13 (2018) doi: 10.1186/s12859-018-2033-5
  • Giulio Caravagna, Ylenia Giarratano, Daniele Ramazzotti, Trevor A. Graham, Guido Sanguinetti, Andrea Sottoriva: Detecting repeated cancer evolution in human tumors from multi-region sequencing data. Nature Methods volume 15, pages707–714 (2018) doi: 10.1038/s41592-018-0108-x
  • Ylenia Giarratano, Eleonora Bianchi, Calum Gray, Andrew Morris, Tom MacGillivray, Baljean Dhillon, Miguel O. Bernabeu; Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics. Trans. Vis. Sci. Tech. 2020;9(13):5 https://doi.org/10.1167/tvst.9.13.5.
  • Giarratano, Y., Pavel, A., Lian, J., Andreeva, R., Fontanella, A., Sarkar, R., .. Bernabeu, M. O. (2020). A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA) In: Fu H., Garvin M.K., MacGillivray T., Xu Y., Zheng Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_16
  • Andreeva R., Fontanella A., Giarratano Y., Bernabeu M.O. (2020) DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis. In: Fu H., Garvin M.K., MacGillivray T., Xu Y., Zheng Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_2
  • Dataset: Optical Coherence Tomography Angiography retinal scans and segmentations :https://doi.org/10.7488/ds/2729.

2021: Travel Grant ARVO2021

2021: SINAPSE Image of the Month (January 2021)

2020: Best Presentation Award Runner-Up OMIA7, MICCAI2020

2020: Best Presentation Award SINAPSE 2020 Annual Meeting 

2018: Academic Merit Award, University of Trento

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