Eugene Vorontsov
Orcid: 0000-0002-4530-533X
According to our database1,
Eugene Vorontsov
authored at least 27 papers
between 2014 and 2024.
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Bibliography
2024
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation.
Medical Image Anal., 2024
CoRR, 2024
CoRR, 2024
2023
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation.
CoRR, 2023
M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
2022
Medical Image Anal., 2022
Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
2021
Proceedings of the Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 2021
Managing Class Imbalance in Multi-Organ CT Segmentation in Head and Neck Cancer Patients.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
2019
CoRR, 2019
A large annotated medical image dataset for the development and evaluation of segmentation algorithms.
CoRR, 2019
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019
2018
Medical Image Anal., 2018
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
2017
Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.
Medical Biol. Eng. Comput., 2017
CoRR, 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
Proceedings of the Deep Learning and Data Labeling for Medical Applications, 2016
2015
CoRR, 2015
2014
Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models.
Proceedings of the Abdominal Imaging. Computational and Clinical Applications, 2014