| A novel fused convolutional neural network for biomedical image classification S Pang, A Du, MA Orgun, Z Yu Medical & biological engineering & computing 57, 107-121, 2019 | 92 | 2019 |
| A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images S Pang, Z Yu, MA Orgun Computer methods and programs in biomedicine 140, 283-293, 2017 | 92 | 2017 |
| Deep learning to frame objects for visual target tracking S Pang, JJ del Coz, Z Yu, O Luaces, J Díez Engineering Applications of Artificial Intelligence 65, 406-420, 2017 | 64 | 2017 |
| CTumorGAN: a unified framework for automatic computed tomography tumor segmentation S Pang, A Du, MA Orgun, Z Yu, Y Wang, Y Wang, G Liu European journal of nuclear medicine and molecular imaging 47, 2248-2268, 2020 | 54 | 2020 |
| Tumor attention networks: Better feature selection, better tumor segmentation S Pang, A Du, MA Orgun, Y Wang, Z Yu Neural Networks 140, 203-222, 2021 | 46 | 2021 |
| A novel biomedical image indexing and retrieval system via deep preference learning S Pang, MA Orgun, Z Yu Computer methods and programs in biomedicine 158, 53-69, 2018 | 44 | 2018 |
| Beyond CNNs: exploiting further inherent symmetries in medical image segmentation S Pang, A Du, MA Orgun, Y Wang, QZ Sheng, S Wang, X Huang, Z Yu IEEE Transactions on Cybernetics 53 (11), 6776-6787, 2022 | 21 | 2022 |
| Incremental graph regulated nonnegative matrix factorization for face recognition ZZ Yu, YH Liu, B Li, SC Pang, CC Jia Journal of Applied Mathematics 2014 (1), 928051, 2014 | 17 | 2014 |
| Uniads: Universal architecture-distiller search for distillation gap L Lu, Z Chen, X Lu, Y Rao, L Li, S Pang Proceedings of the AAAI Conference on Artificial Intelligence 38 (13), 14167 …, 2024 | 15 | 2024 |
| Tgnet: A task-guided network architecture for multi-organ and tumour segmentation from partially labelled datasets H Wu, S Pang, A Sowmya 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-5, 2022 | 15 | 2022 |
| Deep learning and preference learning for object tracking: a combined approach S Pang, JJ Del Coz, Z Yu, O Luaces, J Díez Neural Processing Letters 47, 859-876, 2018 | 15 | 2018 |
| Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings S Pang, M Field, J Dowling, S Vinod, L Holloway, A Sowmya Artificial Intelligence in Medicine 123, 102230, 2022 | 13 | 2022 |
| Fast and accurate lung tumor spotting and segmentation for boundary delineation on CT slices in a coarse-to-fine framework S Pang, A Du, X He, J Díez, MA Orgun Neural Information Processing: 26th International Conference, ICONIP 2019 …, 2019 | 13 | 2019 |
| dp-promise: Differentially Private Diffusion Probabilistic Models for Image Synthesis H Wang, S Pang, Z Lu, Y Rao, Y Zhou, M Xue USENIX, 2024 | 11 | 2024 |
| Weakly supervised learning for image keypoint matching using graph convolutional networks S Pang, A Du, MA Orgun, H Chen Knowledge-Based Systems 197, 105871, 2020 | 11 | 2020 |
| PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation A Du, T Zhou, S Pang, Q Wu, J Zhang IEEE Transactions on Multimedia, 2024 | 8 | 2024 |
| Face recognition: a novel deep learning approach SC Pang, ZZ Yu Journal of Optical Technology 82 (4), 237-245, 2015 | 8 | 2015 |
| Combining deep learning and preference learning for object tracking S Pang, JJ Del Coz, Z Yu, O Luaces, J Díez International Conference on Neural Information Processing, 70-77, 2016 | 7 | 2016 |
| Enhancing content-based recommendation via large language model W Xu, Q Xie, S Yang, J Cao, S Pang Proceedings of the 33rd ACM International Conference on Information and …, 2024 | 6 | 2024 |
| An end-to-end weakly supervised learning framework for cancer subtype classification using histopathological slides H Zhou, H Chen, B Yu, S Pang, X Cong, L Cong Expert Systems with Applications 237, 121379, 2024 | 6 | 2024 |
