Y. Gao
Recent publications
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Wang, X., Tan, T., Gao, Y., Marcus, E., Zhou, H.-Y., Lu, C., Han, L., Portaluri, A., Su, R., Zhang, T., Liang, X., Beets-Tan, R., Pinker, K., Sun, Y., Mann, R., & Teuwen, J. (2026). Incorporating global-local tissue changes to predict future breast cancer from longitudinal screening mammograms. Medical Image Analysis, 110, Article 103990. https://doi.org/10.1016/j.media.2026.103990More information about this publication
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Liang, X., Zhang, T., Braga, M., Han, L., Donswijk, M., Huang, J., Song, J., Lu, C., Wang, X., Gao, Y., Xiong, C., Sun, Y., Xu, J., Teuwen, J., Vogel, W., Tan, T., & Mann, R. (2026). Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer. npj Precision Oncology, 10(1), Article 74. https://doi.org/10.1038/s41698-026-01283-7More information about this publication
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Gao, Y., Zhou, H.-Y., Wang, X., Portaluri, A., Zhang, T., Beets-Tan, R., Han, L., Lu, C., Estacio, L., Da'ngelo, A., Ursprung, S., Yu, Y., Teuwen, J., Tan, T., & Mann, R. (2026). Visualizing Radiologic Connections: An Explainable Coarse-to-Fine Foundation Modelwith Multiview Mammograms and Associated Reports. Radiology: Artificial Intelligence, 8(1), Article e240646. https://doi.org/10.1148/ryai.240646More information about this publication
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Wang, X., Tan, T., Gao, Y., Zhou, H.-Y., Zhang, T., Han, L., Portaluri, A., Marcus, E., Lu, C., Drukker, C. A., Teuwen, J., Beets-Tan, R., Wang, S., Karssemeijer, N., & Mann, R. (2025). Mammo-AGE: deep learning estimation of breast age from mammograms. Nature Communications, 16(1), Article 10934. https://doi.org/10.1038/s41467-025-65923-5More information about this publication
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Wang, X., Tan, T., Gao, Y., Su, R., Teuwen, J., Kroes, J., Zhang, T., D'angelo, A., Han, L., Drukker, C. A., Schmidt, M. K., Beets-Tan, R., Karssemeijer, N., & Mann, R. (2025). Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history. npj Breast Cancer, 11(1), Article 118. https://doi.org/10.1038/s41523-025-00831-xMore information about this publication
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Han, L., Tan, T., Huang, Y., Dou, H., Zhang, T., Gao, Y., Wang, X., Lu, C., Liang, X., Sun, Y., Teuwen, J., Zhou, S. K., & Mann, R. (2025). All-in-one medical image-to-image translation. Cell Reports Methods, 5(8), Article 101138. https://doi.org/10.1016/j.crmeth.2025.101138More information about this publication
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Han, L., Zhang, T., D'Angelo, A., van der Voort, A., Pinker-Domenig, K., Kok, M., Sonke, G., Gao, Y., Wang, X., Lu, C., Liang, X., Teuwen, J., Tan, T., & Mann, R. (2025). Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model. EClinicalMedicine, 86, Article 103356. https://doi.org/10.1016/j.eclinm.2025.103356More information about this publication
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Rasoolzadeh, N., Zhang, T., Gao, Y., van Dijk, J. M., Yang, Q., Tan, T., & Mann, R. M. (2025). Multimodal Breast MRI Language-Image Pretraining (MLIP): An Exploration of a Breast MRI Foundation Model. In R. M. Mann, T. Zhang, L. Han, G. Litjens, T. Tan, D. Truhn, S. Li, Y. Gao, S. Doyle, R. Martí Marly, J. N. Kather, K. Pinker-Domenig, & S. Wu (Eds.), Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care - 1st Deep Breast Workshop, Deep-Breath 2024, Held in Conjunction with MICCAI 2024, Proceedings (Vol. 15451 LNCS, pp. 42-53). Springer Verlag. https://doi.org/10.1007/978-3-031-77789-9_5More information about this publication
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Gao, Y., Tan, T., Wang, X., Beets-Tan, R., Zhang, T., Han, L., Portaluri, A., Lu, C., Liang, X., Teuwen, J., Zhou, H. Y., & Mann, R. (2025). Multi-modal Longitudinal Representation Learning for Predicting Neoadjuvant Therapy Response in Breast Cancer Treatment. IEEE Journal of Biomedical and Health Informatics, 29(12), 9041-9050. https://doi.org/10.1109/JBHI.2025.3540574More information about this publication
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Gao, Y., Ventura-Diaz, S., Wang, X., He, M., Xu, Z., Weir, A., Zhou, H. Y., Zhang, T., van Duijnhoven, F. H., Han, L., Li, X., D’Angelo, A., Longo, V., Liu, Z., Teuwen, J., Kok, M., Beets-Tan, R., Horlings, H. M., Tan, T., & Mann, R. (2024). An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer. Nature Communications, 15(1), Article 9613. https://doi.org/10.1038/s41467-024-53450-8More information about this publication