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Understanding multicellular function and disease with human tissue-specific networks. Nature genetics Vol. 47 6 (2015) 569--576.  Casey S Greene Arjun Krishnan Aaron K Wong Emanuela Ricciotti et al. 2015. Understanding multicellular function and disease with human tissue-specific networks. Nature genetics Vol. 47 6 (2015) 569--576.","DOI":"10.1038\/ng.3259"},{"key":"e_1_3_2_2_17_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_2_18_1","volume-title":"Image-to-Image Translation with Conditional Adversarial Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society","author":"Isola P.","year":"2017","unstructured":"P. Isola , J. Zhu , T. Zhou , and A. A. Efros . 2017a . 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IEEE, 117--122."},{"key":"e_1_3_2_2_23_1","volume-title":"United States","author":"Miller Henry W","year":"1971","unstructured":"Henry W Miller . 1973. Plan and operation of the health and nutrition examination survey , United States , 1971 --1973. DHEW publication no.(PHS)-Dept. of Health, Education, and Welfare (USA) ( 1973). Henry W Miller. 1973. Plan and operation of the health and nutrition examination survey, United States, 1971--1973. DHEW publication no.(PHS)-Dept. of Health, Education, and Welfare (USA) (1973)."},{"key":"e_1_3_2_2_24_1","volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero . 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 ( 2014 ). Mehdi Mirza and Simon Osindero. 2014. 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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5--9, 2015, Proceedings, Part I 18. Springer, 556--564."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_2_31_1","volume-title":"Anton Van Den Hengel, and Johan W Verjans","author":"Shen Haifeng","year":"2021","unstructured":"Haifeng Shen , Kewen Liao , Zhibin Liao , Job Doornberg , Maoying Qiao , Anton Van Den Hengel, and Johan W Verjans . 2021 . Human-AI interactive and continuous sensemaking: A case study of image classification using scribble attention maps. In Extended Abstracts of CHI. 1--8. Haifeng Shen, Kewen Liao, Zhibin Liao, Job Doornberg, Maoying Qiao, Anton Van Den Hengel, and Johan W Verjans. 2021. 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