Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
361-02-004 ISMRM Abstract

Prostate Cancer Classification with Multi-Sequence Token Fusion using a 3D MRI Foundation Model

Primary: Analysis Methods - Foundation Models
Secondary: Body - Prostate
361-02-004 · Prostate: Clinical Applications, AI, and Post-Processing · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Prostate cancer AI Foundation Model Data-efficient fine-tuning Multi-sequence MRI Multi-sequence token fusion
Accepted
Sifan Song1, Matthew Tivnan1, Xiang Li1, Kyungsang Kim1, Elshaimaa Sharaf2, Zhijian Yang3, Noel DSouza3, Emanuele Valeriano3, Marc Lebel 3, Erhan Bas3, Parminder Bhatia3, Taha Kass-hout3, Mukesh G Harisinghani4, Marcio A Bezerra Cavalcanti Rockenbach2, Quanzheng Li1
1Center for Advanced Medical Computing and Analysis (CAMCA),, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
2Mass General Brigham AI, United States of America
3GE HealthCare, San Ramon, United States of America
4Massachusetts General Hospital, Boston, United States of America
Presenting Author: Marc Lebel

Synopsis

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References

1. Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J. and Rajpurkar, P., 2023. Foundation models for generalist medical artificial intelligence. Nature, 616(7956), pp.259-265. DOI: 10.1038/s41586-023-05881-4 [doi]
2. Paschali, M., Chen, Z., Blankemeier, L., Varma, M., Youssef, A., Bluethgen, C., Langlotz, C., Gatidis, S. and Chaudhari, A., 2025. Foundation models in radiology: What, how, why, and why not. Radiology, 314(2), p.e240597. DOI: https://doi.org/10.1148/radiol.240597 [doi]
3. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I., 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60-88. DOI: https://doi.org/10.1016/j.media.2017.07.005 [doi]
4. Schäfer, R., Nicke, T., Höfener, H., Lange, A., Merhof, D., Feuerhake, F., Schulz, V., Lotz, J. and Kiessling, F., 2024. Overcoming data scarcity in biomedical imaging with a foundational multi-task model. Nature Computational Science, 4(7), pp.495-509. DOI: https://doi.org/10.1038/s43588-024-00662-z [doi]
5. Ahmed, H.U., Bosaily, A.E.S., Brown, L.C., Gabe, R., Kaplan, R., Parmar, M.K., Collaco-Moraes, Y., Ward, K., Hindley, R.G., Freeman, A. and Kirkham, A.P., 2017. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. The Lancet, 389(10071), pp.815-822. DOI: 10.1016/S0140-6736(16)32401-1 [doi]
6. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J. and Houlsby, N., 2021. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. Proceedings of the 9th International Conference on Learning Representations (ICLR 2021), Virtual Event, 3–7 May 2021. OpenReview.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-02-004