Search results

• 2024

  LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

  Dorent, R., Khajavi, R., Idris, T., Ziegler, E., Somarouthu, B., Jacene, H., LaCasce, A., Deissler, J., Ehrhardt, J., Engelson, S., Fischer, S. M., Gu, Y., Handels, H., Kasai, S., Kondo, S., Maier-Hein, K., Schnabel, J. A., Wang, G., Wang, L. & Wald, T. & 7 others, Yang, G.-Z., Zhang, H., Zhang, M., Pieper, S., Harris, G., Kikinis, R. & Kapur, T., 19 Aug 2024, In: Machine.Learning.for.Biomedical.Imaging..

  Research output: Contribution to journal › Article › peer-review

  File
  15 Downloads (Pure)
• 2023

  CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

  Dorent, R., Kujawa, A., Ivory, M., Bakas, S., Rieke, N., Joutard, S., Glocker, B., Cardoso, J., Modat, M., Batmanghelich, K., Belkov, A., Calisto, M. B., Choi, J. W., Dawant, B. M., Dong, H., Escalera, S., Fan, Y., Hansen, L., Heinrich, M. P. & Joshi, S. & 20 others, Kashtanova, V., Kim, H. G., Kondo, S., Kruse, C. N., Lai-Yuen, S. K., Li, H., Liu, H., Ly, B., Oguz, I., Shin, H., Shirokikh, B., Su, Z., Wang, G., Wu, J., Xu, Y., Yao, K., Zhang, L., Ourselin, S., Shapey, J. & Vercauteren, T., 1 Jan 2023, In: Medical Image Analysis. 83, 102628.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Domain Adaptation 100%
  • Vestibular Schwannoma 100%
  • Acoustic Neuroma 100%
  • Magnetic Resonance Imaging 100%
  • Image Segmentation 20%
  47 Citations (Scopus)

  2 Downloads (Pure)

• Upl-sfda: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

  Wu, J., Wang, G., Gu, R., Lu, T., Chen, Y., Zhu, W., Vercauteren, T., Ourselin, S. & Zhang, S., 1 Dec 2023, In: IEEE transactions on medical imaging. 42, 12, p. 3932-3943 12 p.

  Research output: Contribution to journal › Article › peer-review

  • Image Segmentation 100%
  • Source-Free Domain Adaptation 100%
  • Annotation 16%
  • Segmentation Model 16%
  • Domain Adaptation 16%
  22 Citations (Scopus)
• 2022

  Monai: An open-source framework for deep learning in healthcare

  Cardoso, M. J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., Yang, D., Nath, V., He, Y., Xu, Z., Hatamizadeh, A., Myronenko, A., Zhu, W., Liu, Y., Zheng, M., Tang, Y. & Yang, I. & 37 others, Zephyr, M., Hashemian, B., Alle, S., Darestani, M. Z., Budd, C., Modat, M., Vercauteren, T., Wang, G., Li, Y., Hu, Y., Fu, Y., Gorman, B., Johnson, H., Genereaux, B., Erdal, B. S., Gupta, V., Diaz-Pinto, A., Dourson, A., Maier-Hein, L., Jaeger, P. F., Baumgartner, M., Kalpathy-Cramer, J., Flores, M., Kirby, J., Cooper, L. A. D., Roth, H. R., Xu, D., Bericat, D., Floca, R., Zhou, S. K., Shuaib, H., Farahani, K., Maier-Hein, K. H., Aylward, S., Dogra, P., Ourselin, S. & Feng, A., 4 Nov 2022.

  Research output: Working paper/Preprint › Preprint

  File

  • Clinical Research 100%
  • Diseases 100%
  36 Downloads (Pure)
• 2020

  An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

  Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., Melbourne, A., Doel, T., Dymarkowski, S., De Coppi, P., David, A. L., Deprest, J., Ourselin, S. & Vercauteren, T., 1 Feb 2020, In: NeuroImage. 206, 116324.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Convolutional Neural Network 100%
  • super resolution 100%
  • Reconstruction Result 50%
  • User Interaction 50%
  • Quality Assessment 50%
  186 Citations (Scopus)

  287 Downloads (Pure)

• CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

  Gu, R., Wang, G., Song, T., Huang, R., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T. & Zhang, S., 2 Nov 2020, (E-pub ahead of print) In: IEEE transactions on medical imaging.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Image Segmentation 100%
  • Convolutional Neural Network 100%
  • Neural Network Architecture 20%
  • Art Performance 20%
  • Lesion Segmentation 20%
  530 Citations (Scopus)
• 2019

  Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

  Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S. & Vercauteren, T., 21 Apr 2019, In: NEUROCOMPUTING. 338, p. 34-45 12 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Test Time 100%
  • Medical Image Segmentation 100%
  • Aleatoric Uncertainty 100%
  • Convolutional Neural Network 100%
  • Neural Network 100%
  462 Citations (Scopus)

• An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI

  Shapey, J., Wang, G., Dorent, R., Dimitriadis, A., Li, W., Paddick, I., Kitchen, N., Bisdas, S., Saeed, S. R., Ourselin, S., Bradford, R. & Vercauteren, T., 6 Dec 2019, (E-pub ahead of print) In: Journal of Neurosurgery. p. 1-9 9 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Automatic Segmentation 100%
  • Artificial Intelligence 100%
  • Magnetic Resonance Imaging 100%
  • Vestibular Schwannoma 100%
  • Volumetry 100%
  84 Citations (Scopus)

  75 Downloads (Pure)

• Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation

  Wang, G., Li, W., Ourselin, S. & Vercauteren, T., 13 Aug 2019, In: Frontiers in Computational Neuroscience. 13, 56.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Tumor Segmentation 100%
  • Convolutional Neural Network 100%
  • Uncertainty Estimation 100%
  • Neural Network 100%
  • Intracranial Tumor 100%
  187 Citations (Scopus)

  326 Downloads (Pure)
• 2018

  An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI

  Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., Melbourne, A., Doel, T., David, A. L., Deprest, J., Ourselin, S. & Vercauteren, T., 2018, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Schnabel, J. A., Davatzikos, C., Alberola-López, C., Fichtinger, G. & Frangi, A. F. (eds.). Springer Verlag, Vol. 11070. p. 313-320 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11070 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Convolutional Neural Network 100%
  • super resolution 100%
  • User Interaction 50%
  • Single Parameter 50%
  42 Citations (Scopus)

• Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches

  Slator, P., Aughwane, R., Cade, G., Taylor, D., David, A. L., Lewis, R., Jauniaux, E., Desjardins, A., Salomon, L. J., Millischer, A.-E., Tsatsaris, V., Rutherford, M., Johnstone, E., Melbourne, A., Atkinson, D., Baranikumar, R., Bertholdt, C., Bonet-Carne, E., Brownbill, P. & Bruchhage, M. & 42 others, Caulfield, R., Chernyavsky, I., Chew, A., David, A., De Vita, E., Doel, T., Erlich, A., Flouri, D., Guerreri, M., Hakim, M., Hansen, D., Haq, M., Haris, P., Hillman, S., Ho, A., Hutter, J., Jackson, L., Johnstone, E., Kipergil, E., Labianco, S., Malamateniou, C., Maneas, E., Monton, E., Morris, D., Nihouarn, J., Nye, G., O'Neill, H., Thunbo, M. Ø., Palombo, M., Peasley, R., Baruteau, K. P., Mayo, R. P., Port, S., Salomon, L., Shah, S., Soe, N., Soerensen, A., Sokolska, M., Svigilsky, C., Tropea, T., Wang, G. & Yassine, B., 31 Oct 2018, (E-pub ahead of print) In: Placenta. 79, p. 78-82

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Placenta 100%
  • Imaging 100%
  • Magnetic Resonance Imaging 100%
  • Clinician 33%
  • Nuclear Magnetic Resonance Imaging 33%
  6 Citations (Scopus)

  154 Downloads (Pure)