Machine learning ZH Zhou Springer nature, 2021 | 2410 | 2021 |
Transformers in medical imaging: A survey F Shamshad, S Khan, SW Zamir, MH Khan, M Hayat, FS Khan, H Fu Medical Image Analysis 88, 102802, 2023 | 483 | 2023 |
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III N Navab, J Hornegger, WM Wells, A Frangi Springer, 2015 | 404 | 2015 |
Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation J Zilly, JM Buhmann, D Mahapatra Computerized Medical Imaging and Graphics 55, 28-41, 2017 | 396 | 2017 |
Image super-resolution using progressive generative adversarial networks for medical image analysis D Mahapatra, B Bozorgtabar, R Garnavi Computerized Medical Imaging and Graphics 71, 30-39, 2019 | 256 | 2019 |
Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network D Mahapatra, B Bozorgtabar, JP Thiran, M Reyes International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 188 | 2018 |
Deformable medical image registration using generative adversarial networks D Mahapatra, B Antony, S Sedai, R Garnavi 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018 | 180 | 2018 |
Unsupervised medical image translation with adversarial diffusion models M Özbey, O Dalmaz, SUH Dar, HA Bedel, Ş Özturk, A Güngör, T Çukur IEEE Transactions on Medical Imaging, 2023 | 172 | 2023 |
Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma N Thakur, M Juneja Biomedical Signal Processing and Control 42, 162-189, 2018 | 139 | 2018 |
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives J Li, J Chen, Y Tang, C Wang, BA Landman, SK Zhou Medical image analysis 85, 102762, 2023 | 131 | 2023 |
MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge R Verma, N Kumar, A Patil, NC Kurian, S Rane, S Graham, QD Vu, ... IEEE Transactions on Medical Imaging 40 (12), 3413-3423, 2021 | 125 | 2021 |
Prostate MRI Segmentation Using Learned Semantic Knowledge And Graph Cuts D Mahapatra IEEE Transactions on Biomedical Engineering 61 (3), 756-764, 2014 | 125 | 2014 |
Unsupervised misaligned infrared and visible image fusion via cross-modality image generation and registration D Wang, J Liu, X Fan, R Liu arXiv preprint arXiv:2205.11876, 2022 | 120 | 2022 |
Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis D Mahapatra, B Bozorgtabar, S Hewavitharanage, R Garnavi Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 108 | 2017 |
Analyzing Training Information From Random Forests For Improved Image Segmentation D Mahapatra Transactions on Image Processing 23 (4), 1504-1512, 2014 | 108 | 2014 |
Image registration based on autocorrelation of local structure Z Li, D Mahapatra, JAW Tielbeek, J Stoker, LJ van Vliet, FM Vos IEEE transactions on medical imaging 35 (1), 63-75, 2015 | 107 | 2015 |
Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings Y Shi, HI Suk, M Liu Springer, 2018 | 105 | 2018 |
Integrating segmentation information for improved MRF-based elastic image registration D Mahapatra, Y Sun IEEE Transactions on Image Processing 21 (1), 170-183, 2011 | 105 | 2011 |
Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images JG Zilly, JM Buhmann, D Mahapatra Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015 …, 2015 | 99 | 2015 |
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI D Mahapatra, PJ Schüffler, JAW Tielbeek, JC Makanyanga, J Stoker, ... IEEE Transactions on Medical Imaging 32 (12), 2332-2348, 2013 | 99 | 2013 |