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A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Cerri, S., Munk, A., Ambsdorf, J., Machnio, J., Nersesjan, V., Krag, C. H., Liu, P., García, P. R., Ghazi, M. M., Boesen, M., Benros, M. E., Iglesias, J. E. & Nielsen, M., 2026, arXiv.org, 43 s.Publikation: Working paper › Preprint
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General Methods Make Great Domain-Specific Foundation Models: A Case-Study on Fetal Ultrasound
Ambsdorf, J., Munk, A., Llambias, S., Christensen, A. N., Mikolaj, K., Balestriero, R., Tolsgaard, M. G., Feragen, A. & Nielsen, M., 2026, Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings, Part VII. Springer, s. 271-281 (Lecture Notes in Computer Science, Bind 15966 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review
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Revisiting Clip: Efficient Alignment of 3D MRI and Tabular Data Using Domain-Specific Foundation Models
Petersen, J. K., Licht, V., Nielsen, M. & Munk, A., 2025, ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings. IEEE Computer Society Press, s. 1-5 (Proceedings - International Symposium on Biomedical Imaging).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review
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MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept
Munk, A., Ma, A. & Nielsen, M., 2024, Proceedings of the 5th Northern Lights Deep Learning Conference (NLDL). PMLR, Bind 233. s. 174-180 (Proceedings of Machine Learning Research, Bind 233).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review
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Yucca: A Deep Learning Framework For Medical Image Analysis
Llambias, S. N., Machnio, J., Munk, A., Ambsdorf, J., Nielsen, M. & Ghazi, M. M., 2024, arXiv.org, 8 s.Publikation: Working paper › Preprint
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