[69] Luciano M Prevedello, Safwan S Halabi, George Shih, Carol C Wu, Marc D Kohli, Falgun H Chokshi, Bradley J Erickson, Jayashree Kalpathy-Cramer, Katherine P Andriole, and Adam E Flanders. Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiology: Artificial Intelligence, 1(1):e180031, 2019.
[70] Pranav Rajpurkar and Matthew P Lungren. The current and future state of ai interpretation of medical images. New England Journal of Medicine, 388(21):1981-1990, 2023.
[71] Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. A survey of deep active learning. arXiv preprint arXiv:2009.00236, 2020.
[72] Blaine Rister, Darwin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin. Ct-org, a new dataset for multiple organ segmentation in computed tomography. Scientific Data, 7(1):1-9, 2020.
[73] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 234-241. Springer, 2015.
[74] Holger R Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu, Evrim B Turkbey, and Ronald M Summers. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In International conference on medical image computing and computer-assisted intervention, pages 556-564. Springer, 2015.
[75] Tobias Scheffer, Christian Decomain, and Stefan Wrobel. Active hidden markov models for information extraction. In International Symposium on Intelligent Data Analysis, pages 309-318. Springer, 2001.
[76] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489, 2017.
[77] Gonglei Shi, Li Xiao, Yang Chen, and S Kevin Zhou. Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Medical Image Analysis, 70:101979, 2021.
[78] George Shih, Carol C Wu, Safwan S Halabi, Marc D Kohli, Luciano M Prevedello, Tessa S Cook, Arjun Sharma, Judith K Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. *Radiology: Artificial Intelligence*, 1(1):e180041, 2019.
[79] Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063, 2019.
[80] Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, and Eyal Klang. Convolutional neural networks for radiologic images: a radiologist's guide. Radiology, 290(3):590-606, 2019.
[81] Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Sila Kurugol, and Simon K Warfield. Active deep learning with fisher information for patch-wise semantic segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pages 83-91. Springer, 2018.
[82] Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Xavier Tomas-Fernandez, Sila Kurugol, and Simon K Warfield. Intelligent labeling based on fisher information for medical image segmentation using deep learning. IEEE transactions on medical imaging, 38(11):2642-2653, 2019.
[83] Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N Chiang, Zhihao Wu, and Xiaowei Ding. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, page 101693, 2020.
[84] Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. Self-supervised pre-training of swim transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20730-20740, 2022.
[85] Emily B Tsai, Scott Simpson, Matthew P Lungren, Michelle Hershman, Leonid Roshkovan, Errol Colak, Bradley J Erickson, George Shih, Anouk Stein, Jayashree Kalpathy-Cramer, et al. The rsna international Covid-19 open radiology database (ricord). Radiology, 299(1):E204-E213, 2021.