Fabian Yii
Thesis title: Harnessing artificial intelligence for pathologic myopia risk prediction

MRC DTP in Precision Medicine
Year of study: 3
Contact details
- Email: fabian.yii@ed.ac.uk
- Web: GitHub
Address
- Street
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Centre for Clinical Brain Sciences
Chancellor's Building
49 Little France Crescent - City
- Edinburgh
- Post code
- EH16 4SB
Qualifications
BSc (Hons) Optometry, Glasgow Caledonian University
Postgraduate teaching
Machine Learning Practical (INFR11132), School of Informatics: Demonstrator/Tutor/Marker
Text Remix (EFIE11004), Edinburgh Futures Institute: Postgraduate Teaching Assistant
Narrative and Computational Text Analysis (EFIE11088), Edinburgh Futures Institute: Postgraduate Teaching Assistant
Conference details
International Visual Field & Imaging Symposium 2022, Berkeley, California: "Power to the People: Estimating the Detectability of Change in Individual Patients’ Visual Field Series" (online oral presentation)
MICCAI Workshop on Ophthalmic Medical Image Analysis 2022, Singapore: "Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter" (poster presentation)
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Yii F (2023) ‘Emmetropic eye growth in East Asians and non-East Asians’ Ophthalmic and Physiological Optics 0, (0) 1-7. https://doi.org/10.1111/opo.13195
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Yii F (2023) ‘Letter to the editor: 20-20-20 rule: are these numbers justified?’ Optometry and Vision Science 100, (5) 346-347. https://doi.org/10.1097/OPX.0000000000002016
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Hu W, Yii F, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, He M (2023) ‘A systematic review and meta-analysis of applying deep learning in the prediction of the risk of cardiovascular diseases from retinal images’ Translational Vision Science & Technology 12, (7) 14. https://doi.org/10.1167/tvst.12.7.14
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Yii F, Dutt R, MacGillivray T, Dhillon B, Bernabeu M, Strang, N (2022) ‘Rethinking retinal image quality: treating quality threshold as a tunable hyperparameter’ In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2022. Lecture Notes in Computer Science 13576. https://doi.org/10.1007/978-3-031-16525-2_8
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Yii F, MacGillivray T, Bernabeu M (2023) 'Data efficiency of Segment Anything Model for optic disc and cup segmentation' (in press)