Dr Gihan Samarasinghe

Arslan J; Samarasinghe G; Benke KK; Sowmya A; Wu Z; Guymer RH; Baird PN, 2020, 'Artificial intelligence algorithms for analysis of geographic atrophy: A review and evaluation', Translational Vision Science and Technology, vol. 9, pp. 1 - 18, http://dx.doi.org/10.1167/tvst.9.2.57

Nakagawa S; Samarasinghe G; Haddaway NR; Westgate MJ; O'Dea RE; Noble DWA; Lagisz M, 2019, 'Research Weaving: Visualizing the Future of Research Synthesis', Trends in Ecology and Evolution, vol. 34, pp. 224 - 238, http://dx.doi.org/10.1016/j.tree.2018.11.007

Samarasinghe G; Lagisz M; Santamouris M; Yenneti K; Upadhyay AK; De La Peña Suarez F; Taunk B; Nakagawa S, 2019, 'A visualized overview of systematic reviews and meta-analyses on low-carbon built environments: An evidence review map', Solar Energy, vol. 186, pp. 291 - 299, http://dx.doi.org/10.1016/j.solener.2019.04.062

Samarasinghe G; Jayasekara MMNN; Elkaduwe D; Ragel RG, 2012, 'Power Aware Instruction Scheduling for Microcontrollers', International Journal of Scientific and Research Publications, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.443.46&rep=rep1&type=pdf

Lagisz M; Samarasinghe G; Santamouris M; Yenneti K; Upadhyay AK; De La Peña Suarez F; Taunk B; Nakagawa S, 'A visualized overview of systematic reviews and meta-analyses on low-carbon built environments: an evidence review map', , http://dx.doi.org/10.31222/osf.io/d34v8

Upadhyay A; Nakagawa-Lagisz L; Samarasinghe G; Nakagawa S; De La Peña Suarez F; Twomey P, 2018, Do green-rated office buildings save operational energy? Rapid review of comparative evidence, SP0008e1, http://builtbetter.org/node/7555

Gharleghi R; Samarasinghe G; Sowmya A; Beier S, 2020, 'Deep Learning for Time Averaged Wall Shear Stress Prediction in Left Main Coronary Bifurcations', in Proceedings - International Symposium on Biomedical Imaging, pp. 818 - 821, http://dx.doi.org/10.1109/ISBI45749.2020.9098715

Kang J; Samarasinghe G; Senanayake U; Conjeti S; Sowmya A, 2019, 'Deep learning for volumetric segmentation in spatio-temporal data: Application to segmentation of prostate in DCE-MRI', in Proceedings - International Symposium on Biomedical Imaging, pp. 61 - 65, http://dx.doi.org/10.1109/ISBI.2019.8759314

Samarasinghe G; Sowmya A; Moses DA, 2016, 'Heterogeneity analysis of prostate peripheral zone lesions in 3.0 Tesla T2-weighted Magnetic Resonance Images', in CARS 2016-Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition, IJCARS, Heidelberg Germany, pp. S121 - S122, presented at CARS 2016-Computer Assisted Radiology and Surgery, Heidelberg Germany, 21 June 2016 - 25 June 2016, http://dx.doi.org/10.1007/s11548-016-1412-5

Samarasinghe G; Sowmya A; Moses DA, 2016, 'Semi-quantitative analysis of prostate perfusion MRI by clustering of pre and post contrast enhancement phases', in Proceedings - International Symposium on Biomedical Imaging, IEEE, Prague, CZECH REPUBLIC, pp. 943 - 947, presented at IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, CZECH REPUBLIC, 13 April 2016 - 16 April 2016, http://dx.doi.org/10.1109/ISBI.2016.7493420

Samarasinghe G; Sowmya A; Moses DA, 2014, 'A Semi-Quantitative Analysis Model with Parabolic Modelling for DCE-MRI Sequences of Prostate', in Bouzerdoum A; Wang L; Ogunbona P; Li W; Phung SL (eds.), Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, IEEE, Wollongong, Australia, pp. 1 - 6, presented at International Conference on Digital Image Computing: Techniques and Applications (DICTA), Wollongong, Australia, 25 November 2014 - 27 November 2014, http://dx.doi.org/10.1109/DICTA.2014.7008092