| Research weaving: visualizing the future of research synthesis S Nakagawa, G Samarasinghe, NR Haddaway, MJ Westgate, RE O’Dea, ... Trends in ecology & evolution 34 (3), 224-238, 2019 | 143 | 2019 |
| Deep learning for segmentation in radiation therapy planning: a review G Samarasinghe, M Jameson, S Vinod, M Field, J Dowling, A Sowmya, ... Journal of Medical Imaging and Radiation Oncology 65 (5), 578-595, 2021 | 37 | 2021 |
| Artificial intelligence algorithms for analysis of geographic atrophy: a review and evaluation J Arslan, G Samarasinghe, KK Benke, A Sowmya, Z Wu, RH Guymer, ... Translational vision science & technology 9 (2), 57-57, 2020 | 25 | 2020 |
| Deep learning for time averaged wall shear stress prediction in left main coronary bifurcations R Gharleghi, G Samarasinghe, A Sowmya, S Beier 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 1-4, 2020 | 21 | 2020 |
| Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images M Ghaffari, G Samarasinghe, M Jameson, F Aly, L Holloway, P Chlap, ... Magnetic resonance imaging 86, 28-36, 2022 | 18 | 2022 |
| Charting the potential of brain computed tomography deep learning systems QD Buchlak, MR Milne, J Seah, A Johnson, G Samarasinghe, B Hachey, ... Journal of Clinical Neuroscience 99, 217-223, 2022 | 16 | 2022 |
| A visualized overview of systematic reviews and meta-analyses on low-carbon built environments: An evidence review map G Samarasinghe, M Lagisz, M Santamouris, K Yenneti, AK Upadhyay, ... Solar Energy 186, 291-299, 2019 | 14 | 2019 |
| Rapid reviews for the built environment: methodology and guidelines M Lagisz, G Samarasinghe, S Nakagawa CRC for Low Carbon Living, 2019 | 8 | 2019 |
| Deep learning applied to automated segmentation of geographic atrophy in fundus autofluorescence images J Arslan, G Samarasinghe, A Sowmya, KK Benke, LAB Hodgson, ... Translational Vision Science & Technology 10 (8), 2-2, 2021 | 6 | 2021 |
| Semi-quantitative analysis of prostate perfusion mri by clustering of pre and post contrast enhancement phases G Samarasinghe, A Sowmya, DA Moses 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 943-947, 2016 | 5 | 2016 |
| A semi-quantitative analysis model with parabolic modelling for dce-mri sequences of prostate G Samarasinghe, A Sowmya, DA Moses 2014 International Conference on Digital Image Computing: Techniques and …, 2014 | 5 | 2014 |
| Deep learning for volumetric segmentation in spatio-temporal data: application to segmentation of prostate in DCE-MRI J Kang, G Samarasinghe, U Senanayake, S Conjeti, A Sowmya 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 61-65, 2019 | 4 | 2019 |
| Model structure uncertainty in the characterization and growth of geographic atrophy J Arslan, KK Benke, G Samarasinghe, A Sowmya, RH Guymer, PN Baird Translational Vision Science & Technology 10 (6), 2-2, 2021 | 3 | 2021 |
| Power Aware Instruction Scheduling for Microcontrollers K Samarasinghe, M Jayasekara, D Elkaduwe, RG Ragel International Journal of Scientific and Research Publications 2 (10), 135-150, 2012 | 2 | 2012 |
| Do green-rated office buildings save operational energy? M Lagisz, G Samarasinghe, S Nakagawa, AK Upadhyay, FDLP Suarez, ... CRC for Low Carbon Living, 2018 | 1 | 2018 |
| Improved Feature Extraction in Multimodal MRI for Computer-Aided Diagnosis of Prostate Cancer G Samarasinghe UNSW Sydney, 2018 | 1 | 2018 |
| Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy QD Buchlak, CHM Tang, JCY Seah, A Johnson, X Holt, GM Bottrell, ... European Radiology, 1-13, 2023 | | 2023 |
| Refined Reconstruction of Global Prostate Segmentation from Patch-wise Coarse Predictions J Kang, A Sowmya, G Samarasinghe, S Pang | | 2021 |
| Automated Grading of Refractive Error from Fundus Images using Deep Learning D Ovens, G Samarasinghe, D Jagadeesh, T Naduvilath, P Sankaridurg, ... Imaging Systems and Applications, ITh3E. 1, 2020 | | 2020 |
| 377 Machine Learning to Predict Hemodynamic Risk in Left Main Bifurcations R Gharleghi, G Samarasinghe, A Sowmya, S Beier Heart, Lung and Circulation 29, S207, 2020 | | 2020 |
Shinichi NakagawaUniversity of New South WalesVerified email at unsw.edu.au
