Official training and validation sets of crossMoDA 2022. All data will be made available online with a permissive non-commercial copyright-license (CC BY-NC-SA 4.0), allowing for data to be shared, distributed and improved upon. If you use the data, please cite: 1. Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., & Vercauteren, T. (2021). Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.9YTJ-5Q73 2. Dorent, R. et al (2022). CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adapt...
Purpose: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-se...
We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality me...
IntroductionIn an earlier study by King’s College London (KCL), a framework for the automatic segmen...
If you use the data, please cite: Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitri...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By enc...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While ...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By enc...
Introduction:Fully automated artificial intelligence (AI) frameworks have recently reached outstandi...
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers,...
The crossMoDA challenge aims to automatically segment the vestibular schwannoma (VS) tumor and cochl...
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic res...
International audienceDomain adaptation is an important task to enable learning when labels are scar...
This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th ...
Most machine learning applications involve a domain shift between data on which a model has initiall...
Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only ...
Purpose: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-se...
We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality me...
IntroductionIn an earlier study by King’s College London (KCL), a framework for the automatic segmen...
If you use the data, please cite: Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitri...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By enc...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While ...
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By enc...
Introduction:Fully automated artificial intelligence (AI) frameworks have recently reached outstandi...
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers,...
The crossMoDA challenge aims to automatically segment the vestibular schwannoma (VS) tumor and cochl...
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic res...
International audienceDomain adaptation is an important task to enable learning when labels are scar...
This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th ...
Most machine learning applications involve a domain shift between data on which a model has initiall...
Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only ...
Purpose: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-se...
We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality me...
IntroductionIn an earlier study by King’s College London (KCL), a framework for the automatic segmen...