{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:39:37Z","timestamp":1783438777428,"version":"3.54.6"},"publisher-location":"Cham","reference-count":89,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732348","type":"print"},{"value":"9783031732355","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73235-5_10","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:01:53Z","timestamp":1727589713000},"page":"168-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["GTP-4o: Modality-Prompted Heterogeneous Graph Learning for\u00a0Omni-Modal Biomedical Representation"],"prefix":"10.1007","author":[{"given":"Chenxin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"10_CR1","unstructured":"https:\/\/gdc.cancer.gov"},{"issue":"7","key":"10_CR2","doi-asserted-by":"publisher","first-page":"4871","DOI":"10.1007\/s11831-022-09758-z","volume":"29","author":"S Ali","year":"2022","unstructured":"Ali, S., Li, J., Pei, Y., Khurram, R., Rehman, K.U., Mahmood, T.: A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal mr image. Archiv. Comput. Methods Eng. 29(7), 4871\u20134896 (2022)","journal-title":"Archiv. Comput. Methods Eng."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chan, T.H., Cendra, F.J., Ma, L., Yin, G., Yu, L.: Histopathology whole slide image analysis with heterogeneous graph representation learning. In: CVPR, pp. 15661\u201315670 (2023)","DOI":"10.1109\/CVPR52729.2023.01503"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Chen, R.J., et al.: Whole slide images are 2D point clouds: context-aware survival prediction using patch-based graph convolutional networks. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 339\u2013349. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_33","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41(4), 757\u2013770 (2022). https:\/\/doi.org\/10.1109\/TMI.2020.3021387","DOI":"10.1109\/TMI.2020.3021387"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134025 (2021)","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134025 (2021)","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40(8), 865\u2013878 (2022)","DOI":"10.1016\/j.ccell.2022.07.004"},{"key":"10_CR9","unstructured":"Chen, Y., Liu, C., Huang, W., Cheng, S., Arcucci, R., Xiong, Z.: Generative text-guided 3d vision-language pretraining for unified medical image segmentation. arXiv preprint arXiv:2306.04811 (2023)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, C., Liu, X., Arcucci, R., Xiong, Z.: Bimcv-r: a landmark dataset for 3d CT text-image retrieval. arXiv preprint arXiv:2403.15992 (2024)","DOI":"10.1007\/978-3-031-72120-5_12"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, W., Xing, X., Yuan, Y.: Medical federated learning with joint graph purification for noisy label learning. MIA (2023)","DOI":"10.1016\/j.media.2023.102976"},{"issue":"25","key":"10_CR12","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1098\/rsif.2008.0014","volume":"5","author":"EA Codling","year":"2008","unstructured":"Codling, E.A., Plank, M.J., Benhamou, S.: Random walk models in biology. J. R. Soc. Interface 5(25), 813\u2013834 (2008)","journal-title":"J. R. Soc. Interface"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Ding, Z., Dong, Q., Xu, H., Li, C., Ding, X., Huang, Y.: Unsupervised anomaly segmentation for\u00a0brain lesions using dual semantic-manifold reconstruction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022, Part III, pp. 133\u2013144. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-30111-7_12","DOI":"10.1007\/978-3-031-30111-7_12"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Doyle, S., Hwang, M., Shah, K., Madabhushi, A., Feldman, M., Tomaszeweski, J.: Automated grading of prostate cancer using architectural and textural image features. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1284\u20131287. IEEE (2007)","DOI":"10.1109\/ISBI.2007.357094"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Gao, P., et al.: Dynamic fusion with intra-and inter-modality attention flow for visual question answering. In: CVPR, pp. 6639\u20136648 (2019)","DOI":"10.1109\/CVPR.2019.00680"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"He, Z., Li, W., Zhang, T., Yuan, Y.: H 2 gm: a hierarchical hypergraph matching framework for brain landmark alignment. In: MICCAI, pp. 548\u2013558 (2023)","DOI":"10.1007\/978-3-031-43999-5_52"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Hou, W., et al.: H2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. Proc. AAAI Conf. Artif. Intell. 36, 933\u2013941 (2022)","DOI":"10.1609\/aaai.v36i1.19976"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, pp. 2704\u20132710 (2020)","DOI":"10.1145\/3366423.3380027"},{"issue":"1","key":"10_CR19","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41746-020-00341-z","volume":"3","author":"SC Huang","year":"2020","unstructured":"Huang, S.C., Pareek, A., Seyyedi, S., Banerjee, I., Lungren, M.P.: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit. Med. 3(1), 136 (2020)","journal-title":"NPJ Digit. Med."},{"key":"10_CR20","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Joo, S., et al.: Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci. Rep. 11(1), 18800 (2021)","DOI":"10.1038\/s41598-021-98408-8"},{"key":"10_CR22","first-page":"5141","volume":"37","author":"S Kim","year":"2023","unstructured":"Kim, S., Lee, N., Lee, J., Hyun, D., Park, C.: Heterogeneous graph learning for multi-modal medical data analysis. Proc. AAAI Conf. Artif. Intell. 37, 5141\u20135150 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10_CR23","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"10_CR24","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"1","key":"10_CR25","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1109\/TMI.2019.2923601","volume":"39","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Fulham, M., Feng, D., Kim, J.: Co-learning feature fusion maps from PET-CT images of lung cancer. IEEE Trans. Med. Imaging 39(1), 204\u2013217 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Lee, Y.L., Tsai, Y.H., Chiu, W.C., Lee, C.Y.: Multimodal prompting with missing modalities for visual recognition. In: CVPR, pp. 14943\u201314952 (2023)","DOI":"10.1109\/CVPR52729.2023.01435"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Li, C., Feng, B.Y., Fan, Z., Pan, P., Wang, Z.: Steganerf: embedding invisible information within neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 441\u2013453 (2023)","DOI":"10.1109\/ICCV51070.2023.00047"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Endosparse: real-time sparse view synthesis of endoscopic scenes using gaussian splatting. arXiv preprint arXiv:2407.01029 (2024)","DOI":"10.1007\/978-3-031-72089-5_24"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Knowledge condensation distillation. In: European Conference on Computer Vision, pp. 19\u201335. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-20083-0_2"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Domain generalization on medical imaging classification using episodic training with task augmentation. Comput. Biol. Med. 141, 105144 (2022)","DOI":"10.1016\/j.compbiomed.2021.105144"},{"key":"10_CR31","unstructured":"Li, C., et al.: Gaussianstego: a generalizable stenography pipeline for generative 3d gaussians splatting. arXiv preprint arXiv:2407.01301 (2024)"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Endora: video generation models as endoscopy simulators. arXiv preprint arXiv:2403.11050 (2024)","DOI":"10.1007\/978-3-031-72089-5_22"},{"key":"10_CR33","unstructured":"Li, C., Liu, X., Li, W., Wang, C., Liu, H., Yuan, Y.: U-kan makes strong backbone for medical image segmentation and generation. arXiv preprint arXiv:2406.02918 (2024)"},{"key":"10_CR34","unstructured":"Li, C., et al.: Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation. Neural Comput. Appl. 1\u201314 (2022)"},{"key":"10_CR35","unstructured":"Li, C., Zhang, Y., Li, J., Huang, Y., Ding, X.: Unsupervised anomaly segmentation using image-semantic cycle translation. arXiv preprint arXiv:2103.09094 (2021)"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, Y., Liang, Z., Ma, W., Huang, Y., Ding, X.: Consistent posterior distributions under vessel-mixing: a regularization for cross-domain retinal artery\/vein classification. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 61\u201365. IEEE (2021)","DOI":"10.1109\/ICIP42928.2021.9506148"},{"key":"10_CR37","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Li, W., Chen, Z., Li, B., Zhang, D., Yuan, Y.: HTD: heterogeneous task decoupling for two-stage object detection. TIP (2021)","DOI":"10.1109\/TIP.2021.3126423"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Li, W., Guo, X., Yuan, Y.: Novel scenes & classes: towards adaptive open-set object detection. In: ICCV, pp. 15780\u201315790 (2023)","DOI":"10.1109\/ICCV51070.2023.01446"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, J., Han, B., Yuan, Y.: Adjustment and alignment for unbiased open set domain adaptation. In: CVPR, pp. 24110\u201324119 (2023)","DOI":"10.1109\/CVPR52729.2023.02309"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yao, X., Yuan, Y.: Scan: cross domain object detection with semantic conditioned adaptation. In: AAAI, pp. 1421\u20131428 (2022)","DOI":"10.1609\/aaai.v36i2.20031"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yuan, Y.: Sigma: semantic-complete graph matching for domain adaptive object detection. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00522"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yuan, Y.: Sigma++: improved semantic-complete graph matching for domain adaptive object detection. In: TPAMI (2023)","DOI":"10.1109\/CVPR52688.2022.00522"},{"issue":"12","key":"10_CR44","doi-asserted-by":"publisher","first-page":"4023","DOI":"10.1109\/TMI.2020.3008871","volume":"39","author":"X Li","year":"2020","unstructured":"Li, X., Jia, M., Islam, M.T., Yu, L., Xing, L.: Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis. IEEE Trans. Med. Imaging 39(12), 4023\u20134033 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Liang, Z., et al.: Unsupervised large-scale social network alignment via cross network embedding. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 1008\u20131017 (2021)","DOI":"10.1145\/3459637.3482310"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Liberzon, A., et al.: The molecular signatures database hallmark gene set collection. Cell Syst. 1(6), 417\u2013425 (2015)","DOI":"10.1016\/j.cels.2015.12.004"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Lipkova, J., et al.: Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40(10), 1095\u20131110 (2022)","DOI":"10.1016\/j.ccell.2022.09.012"},{"key":"10_CR48","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.663495","volume":"12","author":"D Liu","year":"2021","unstructured":"Liu, D., Yang, X., Wu, X.: Tumor immune microenvironment characterization identifies prognosis and immunotherapy-related gene signatures in melanoma. Front. Immunol. 12, 663495 (2021)","journal-title":"Front. Immunol."},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, Y., Li, C., Li, W., Yuan, Y.: LGS: a light-weight 4d gaussian splatting for efficient surgical scene reconstruction. arXiv preprint arXiv:2406.16073 (2024)","DOI":"10.1007\/978-3-031-72384-1_62"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Stereo vision meta-lens-assisted driving vision. ACS Photonics (2024)","DOI":"10.1021\/acsphotonics.3c01594"},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, W., Yang, Q., Li, B., Yuan, Y.: Towards robust adaptive object detection under noisy annotations. In: CVPR, pp. 14207\u201314216 (2022)","DOI":"10.1109\/CVPR52688.2022.01381"},{"key":"10_CR52","doi-asserted-by":"publisher","unstructured":"Liu, X., Li, W., Yuan, Y.: Intervention and interaction federated abnormality detection with\u00a0noisy clients. In: Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part VIII, pp. 309\u2013319. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_30","DOI":"10.1007\/978-3-031-16452-1_30"},{"key":"10_CR53","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, W., Yuan, Y.: Decoupled unbiased teacher for source-free domain adaptive medical object detection. IEEE Trans. Neural Netw. Learn. Syst. 35(6), 7287\u20137298 (2024)","DOI":"10.1109\/TNNLS.2023.3272389"},{"key":"10_CR54","doi-asserted-by":"crossref","unstructured":"Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: Efficientvit: memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14420\u201314430 (2023)","DOI":"10.1109\/CVPR52729.2023.01386"},{"issue":"7","key":"10_CR55","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1109\/TMI.2022.3150435","volume":"41","author":"X Liu","year":"2022","unstructured":"Liu, X., Yuan, Y.: A source-free domain adaptive polyp detection framework with style diversification flow. IEEE Trans. Med. Imaging 41(7), 1897\u20131908 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR56","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, C., Yang, C., Yuan, Y.: Endogaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561 (2024)","DOI":"10.1007\/978-3-031-72384-1_62"},{"issue":"6","key":"10_CR57","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"issue":"5","key":"10_CR58","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1038\/nrc3261","volume":"12","author":"A Marusyk","year":"2012","unstructured":"Marusyk, A., Almendro, V., Polyak, K.: Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12(5), 323\u2013334 (2012)","journal-title":"Nat. Rev. Cancer"},{"key":"10_CR59","doi-asserted-by":"crossref","unstructured":"Peng, X., Wei, Y., Deng, A., Wang, D., Hu, D.: Balanced multimodal learning via on-the-fly gradient modulation. In: CVPR, pp. 8238\u20138247 (2022)","DOI":"10.1109\/CVPR52688.2022.00806"},{"key":"10_CR60","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"6","key":"10_CR61","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96\u2013108 (2017)","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"10_CR62","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy, L., Xiang, Y., Xie, Z., Tao, C., Zhi, D.: Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit. Med. 4(1), 86 (2021)","journal-title":"NPJ Digit. Med."},{"key":"10_CR63","unstructured":"Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)"},{"key":"10_CR64","doi-asserted-by":"crossref","unstructured":"Sun, L., et al.: Few-shot medical image segmentation using a global correlation network with discriminative embedding. Comput. Biol. Med. 140, 105067 (2022)","DOI":"10.1016\/j.compbiomed.2021.105067"},{"issue":"11","key":"10_CR65","first-page":"992","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. VLDB 4(11), 992\u20131003 (2011)","journal-title":"VLDB"},{"issue":"18","key":"10_CR66","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1093\/bioinformatics\/btab185","volume":"37","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Li, R., Wang, M., Li, A.: Gpdbn: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction. Bioinformatics 37(18), 2963\u20132970 (2021)","journal-title":"Bioinformatics"},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Online disease diagnosis with inductive heterogeneous graph convolutional networks. In: Proceedings of the Web Conference 2021, pp. 3349\u20133358 (2021)","DOI":"10.1145\/3442381.3449795"},{"key":"10_CR68","unstructured":"Wuyang, L., Chen, Y., Jie, L., Xinyu, L., Xiaoqing, G., Yixuan, Y.: Joint polyp detection and segmentation with heterogeneous endoscopic data. In: 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021): Co-located with the 17th IEEE International Symposium on Biomedical Imaging (ISBI 2021), pp. 69\u201379. CEUR-WS Team (2021)"},{"key":"10_CR69","doi-asserted-by":"crossref","unstructured":"Xu, H., Li, C., Zhang, L., Ding, Z., Lu, T., Hu, H.: Immunotherapy efficacy prediction through a feature re-calibrated 2.5 d neural network. Comput. Methods Prog. Biomed. 249, 108135 (2024)","DOI":"10.1016\/j.cmpb.2024.108135"},{"key":"10_CR70","unstructured":"Xu, H., Zhang, Y., Sun, L., Li, C., Huang, Y., Ding, X.: AFSC: adaptive Fourier space compression for anomaly detection. arXiv preprint arXiv:2204.07963 (2022)"},{"key":"10_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104739","volume":"110","author":"R Xu","year":"2022","unstructured":"Xu, R., Li, Y., Wang, C., Xu, S., Meng, W., Zhang, X.: Instance segmentation of biological images using graph convolutional network. Eng. Appl. Artif. Intell. 110, 104739 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10_CR72","doi-asserted-by":"crossref","unstructured":"Xu, Y., Chen, H.: Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 21241\u201321251 (2023)","DOI":"10.1109\/ICCV51070.2023.01942"},{"key":"10_CR73","doi-asserted-by":"crossref","unstructured":"Xue, L., et al.: Ulip: learning a unified representation of language, images, and point clouds for 3d understanding. In: CVPR, pp. 1179\u20131189 (2023)","DOI":"10.1109\/CVPR52729.2023.00120"},{"key":"10_CR74","doi-asserted-by":"crossref","unstructured":"Xue, Z., Marculescu, R.: Dynamic multimodal fusion. In: CVPR, pp. 2574\u20132583 (2023)","DOI":"10.1109\/CVPRW59228.2023.00256"},{"issue":"10","key":"10_CR75","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1109\/TMI.2022.3175478","volume":"41","author":"Q Yang","year":"2022","unstructured":"Yang, Q., Guo, X., Chen, Z., Woo, P.Y., Yuan, Y.: D2-net: dual disentanglement network for brain tumor segmentation with missing modalities. IEEE Trans. Med. Imaging 41(10), 2953\u20132964 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR76","doi-asserted-by":"crossref","unstructured":"Yang, Q., Li, W., Li, B., Yuan, Y.: MRM: masked relation modeling for medical image pre-training with genetics. In: ICCV, pp. 21452\u201321462 (2023)","DOI":"10.1109\/ICCV51070.2023.01961"},{"key":"10_CR77","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/978-3-030-72087-2_39","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Q Yang","year":"2021","unstructured":"Yang, Q., Yuan, Y.: Learning dynamic convolutions for multi-modal 3D MRI brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 441\u2013451. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72087-2_39"},{"key":"10_CR78","doi-asserted-by":"crossref","unstructured":"Zeng, Y., et al.: Exploration of the immune cell infiltration-related gene signature in the prognosis of melanoma. Aging (Albany, NY) 13(3), 3459 (2021)","DOI":"10.18632\/aging.202279"},{"key":"10_CR79","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 793\u2013803 (2019)","DOI":"10.1145\/3292500.3330961"},{"key":"10_CR80","unstructured":"Zhang, S., et\u00a0al.: Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023)"},{"key":"10_CR81","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: Modality-aware mutual learning for multi-modal medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 589\u2013599. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_56","DOI":"10.1007\/978-3-030-87193-2_56"},{"key":"10_CR82","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fang, Q., Qian, S., Xu, C.: Multi-modal multi-relational feature aggregation network for medical knowledge representation learning. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3956\u20133965 (2020)","DOI":"10.1145\/3394171.3413736"},{"key":"10_CR83","unstructured":"Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. In: Machine Learning for Healthcare Conference, pp. 2\u201325. PMLR (2022)"},{"key":"10_CR84","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: Generator versus segmentor: pseudo-healthy synthesis. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 150\u2013160. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_15","DOI":"10.1007\/978-3-030-87231-1_15"},{"key":"10_CR85","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: A graph-transformer for whole slide image classification. IEEE Trans. Med. Imaging 41(11), 3003\u20133015 (2022)","DOI":"10.1109\/TMI.2022.3176598"},{"key":"10_CR86","doi-asserted-by":"crossref","unstructured":"Zhou, F., Chen, H.: Cross-modal translation and alignment for survival analysis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21485\u201321494 (2023)","DOI":"10.1109\/ICCV51070.2023.01964"},{"key":"10_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)","journal-title":"Array"},{"key":"10_CR88","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 (2023)"},{"key":"10_CR89","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7234\u20137242 (2017)","DOI":"10.1109\/CVPR.2017.725"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73235-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T21:15:40Z","timestamp":1732828540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73235-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031732348","9783031732355"],"references-count":89,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73235-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}