X. Wang
Recente publicaties
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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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Wang, Z., Gao, H., Wang, X., Grzegorzek, M., Li, J., Sun, H., Ma, Y., Zhang, X., Zhang, Z., Dekker, A., Traverso, A., Zhang, Z., Qian, L., Xiao, M., & Feng, Y. (2024). Correction: A multi-task learning based applicable AI model simultaneously predicts stage, histology, grade and LNM for cervical cancer before surgery ( vol 24 , 425 , 2024 ). BMC Women's Health, 24(1), Article 602. https://doi.org/10.1186/s12905-024-03435-yMeer 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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Wang, Z., Gao, H., Wang, X., Grzegorzek, M., Li, J., Sun, H., Ma, Y., Zhang, X., Zhang, Z., Dekker, A., Traverso, A., Zhang, Z., Qian, L., Xiao, M., & Feng, Y. (2024). A multi-Task Learning based applicable AI model simultaneously predicts stage, histology, grade and LNM for cervical cancer before surgery. BMC Women's Health, 24(1), Article 425. https://doi.org/10.1186/s12905-024-03270-1Meer 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