Y. Gao
Recente publicaties
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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_5Meer informative over deze publicatie
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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. Advance online publication. https://doi.org/10.1109/JBHI.2025.3540574Meer informative over deze publicatie
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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/s41467-024-53450-8Meer informative over deze publicatie
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Zhang, T., Tan, T., Han, L., Wang, X., Gao, Y., van Dijk, J., Portaluri, A., Gonzalez-Huete, A., D'Angelo, A., Lu, C., Teuwen, J., Beets-Tan, R., Sun, Y., & Mann, R. (2024). IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data. Information Fusion, 108, Article 102381. https://doi.org/10.1016/j.inffus.2024.102381Meer informative over deze publicatie
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Cao, R., Liu, Y., Wen, X., Liao, C., Wang, X., Gao, Y., & Tan, T. (2024). Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images. iScience, 27(5), Article 109712. https://doi.org/10.1016/j.isci.2024.109712Meer informative over deze publicatie
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Han, L., Tan, T., Zhang, T., Huang, Y., Wang, X., Gao, Y., Teuwen, J., & Mann, R. (2024). Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI. Medical Image Analysis, 92, Article 103044. https://doi.org/10.1016/j.media.2023.103044Meer informative over deze publicatie
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Gao, Y., Zhou, H.-Y., Wang, X., Zhang, T., Han, L., Lu, C., Liang, X., Teuwen, J., Beets-Tan, R., Tan, T., Mann, R., Linguraru, MG., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., & Schnabel, JA. (2024). Improving Neoadjuvant Therapy Response Prediction by Integrating Longitudinal Mammogram Generation with Cross-Modal Radiological Reports: A Vision-Language Alignment-Guided Model. In M. G. Linguraru, A. Feragen, B. Glocker, J. A. Schnabel, Q. Dou, S. Giannarou, & K. Lekadir (Eds.), MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION: MICCAI 2024, PT I (Vol. 15001, pp. 133-143). Springer. https://doi.org/10.1007/978-3-031-72378-0_13Meer informative over deze publicatie
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Han, L., Tan, T., Zhang, T., Wang, X., Gao, Y., Lu, C., Liang, X., Dou, H., Huang, Y., Mann, R., Linguraru, MG., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., & Schnabel, JA. (2024). Non-adversarial Learning: Vector-Quantized Common Latent Space for Multi-sequence MRI. In M. G. Linguraru, A. Feragen, B. Glocker, J. A. Schnabel, Q. Dou, S. Giannarou, & K. Lekadir (Eds.), MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION: MICCAI 2024, PT XI (Vol. 15011, pp. 481-491). Springer. https://doi.org/10.1007/978-3-031-72120-5_45Meer informative over deze publicatie
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Wang, X., Tan, T., Gao, Y., Marcus, E., Han, L., Portaluri, A., Zhang, T., Lu, C., Liang, X., Beets-Tan, R., Teuwen, J., & Mann, R. (2024). Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms. In M. G. Linguraru, A. Feragen, B. Glocker, J. A. Schnabel, Q. Dou, S. Giannarou, & K. Lekadir (Eds.), MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT I (Vol. 15001, pp. 155-165). Springer. https://doi.org/10.1007/978-3-031-72378-0_15Meer informative over deze publicatie
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Zhang, T., Tan, T., Samperna, R., Li, Z., Gao, Y., Wang, X., Han, L., Yu, Q., Beets-Tan, R. G. H., & Mann, R. M. (2023). Radiomics and artificial intelligence in breast imaging: a survey. Artificial Intelligence Review, 56(SUPPL 1), 857-892. https://doi.org/10.1007/s10462-023-10543-yMeer informative over deze publicatie