David Morrison
Mr David Morrison
Lecturer - School of Computer Science
Email: dm236@st-andrews.ac.uk
Office: JH104
Phone: +44 (0)1334 463260
David Morrison is a Research Assistant working with Dr David Harris-Birtill on a project to apply zero-touch technologies in a clinical environment. David graduated from Abertay University's Computer Games Technology degree in 2003. He spent the next ten years working as a designer and programmer in the games industry with companies such as VIS, Zoe Mode, Ubisoft Reflections and Outplay Entertainment. He also independently released a number of titles for the then emerging mobile phone games market on platforms from J2ME to iOS.
In 2014, he moved into the field of Civic Technology and Open Data as a Code for Europe Technology Fellow for that year. As part of this project, he produced Go Explore; an open source wildlife and walking guide for East Lothian, based on Local Authority geographical data - currently available from the Apple App Store and Google Play.
Since the completion of the Code for Europe Fellowship, David has worked as a freelance app and website designer. He specialises in working with large institutional data sets, making them accessible to the public in highly visual and interactive ways. This has led him to work with public sector institutions such as the University of Edinburgh and Edinburgh City Council. His most recent project is an app for use by the Drug and Alcohol Recovery Community in Edinburgh.
You can find our more about David and his interests on his personal page.
Recent Publications
- Mohammadi, M, Fell, C, Morrison, D, Syed, S, Konanahalli, P, Bell, S, Bryson, G, Arandjelović, O, Harrison, DJ & Harris-Birtill, D 2024, 'Automated reporting of cervical biopsies using artificial intelligence', PLOS Digital Health, vol. 3, no. 4, e0000381. https://doi.org/10.1371/journal.pdig.0000381
- Bell, S, Blackwood, JD, Fell, C, Mohammadi, M, Morrison, D, Harris-Birtill, D & Bryson, G 2023, 'An overview of artificial intelligence applications for next-generation gynaecological pathology', Diagnostic Histopathology, vol. 29, no. 10, pp. 442-449. https://doi.org/10.1016/j.mpdhp.2023.07.002
- Fell, C, Mohammadi, M, Morrison, D, Arandjelović, O, Syed, S, Konanahalli, P, Bell, S, Bryson, G, Harrison, DJ & Harris-Birtill, D 2023, 'Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence', PLoS ONE, vol. 18, no. 3, e0282577. https://doi.org/10.1371/journal.pone.0282577
- Connor, R, Dearle, A, Morrison, D & Chávez, E 2023, Similarity search with multiple-object queries. in O Pedreira & V Estivill-Castro (eds), Similarity Search and Applications: 16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14289 LNCS, Springer, Cham, pp. 223-237, 16th International Conference on Similarity Search and Applications, SISAP 2023, Coruna, Spain, 9/10/23. https://doi.org/10.1007/978-3-031-46994-7_19
- Morrison, D & Harris-Birtill, DCC 2022, Anonymising pathology data using generative adversarial networks. in JE Tomaszewski, AD Ward & RM (eds), Medical imaging 2022: digital and computational pathology., 1203917, Proceedings of SPIE, vol. 12039, SPIE, Bellingham, WA, SPIE Medical Imaging 2022, San Diego, California, United States, 20/02/22. https://doi.org/10.1117/12.2611803
- Pirzada, P, Morrison, D, Doherty, GH, Dhasmana, DJ & Harris-Birtill, DCC 2022, 'Automated Remote Pulse Oximetry System (ARPOS)', Sensors, vol. 21, no. 13, 4974. https://doi.org/10.3390/s22134974
- Fell, C, Mohammadi, M, Morrison, D, Arandjelovic, O, Caie, P & Harris-Birtill, D 2022, 'Reproducibility of deep learning in digital pathology whole slide image analysis', PLOS Digital Health, vol. 1, no. 12, e0000145. https://doi.org/10.1371/journal.pdig.0000145
- Mohammadi, M, Cooper, J, Arandelovic, O, Fell, CM, Morrison, D, Syed, S, Konanahalli, P, Bell, S, Bryson, G, Harrison, DJ & Harris-Birtill, DCC 2022, 'Weakly supervised learning and interpretability for endometrial whole slide image diagnosis', Experimental Biology and Medicine, vol. 247, no. 22, pp. 2025 - 2037. https://doi.org/10.1177/15353702221126560
- Morrison, D, Harris-Birtill, D & Caie, PD 2021, 'Generative deep learning in digital pathology workflows', The American Journal of Pathology, vol. 191, no. 10, pp. 1717-1723. https://doi.org/10.1016/j.ajpath.2021.02.024