Y. Gao
Recent publications
-
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
-
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.2025.3540574More information about this publication
-
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
-
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.2024.102381More information about this publication
-
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.2024.109712More information about this publication
-
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.2023.103044More information about this publication
-
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_13More information about this publication
-
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_45More information about this publication
-
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_15More information about this publication
-
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-yMore information about this publication