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Annotating medical images, particularly for organ segmentation, is laborious and time-consuming.
Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P Engelson · 1995
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Employing em and pool-based active learning for text classification
Andrew Kachites McCallumzy and Kamal Nigamy · 1998
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Modeling the shape of the scene: A holistic representation of the spatial envelope
Aude Oliva and Antonio Torralba · 2001
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
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Microsoft research cambridge (msrc) object recognition image database (version 2.0), 2004
A Criminisi · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum · 2005
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Margin based active learning
Maria-Florina Balcan, Andrei Broder, and Tong Zhang · 2007
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Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults
Daniel S Marcus, Tracy H Wang, Jamie Parker, John G Csernansky, John C Morris, and Randy L Buckner · 2007
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The alzheimer’s disease neuroimaging initiative (adni): Mri methods
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
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The cancer imaging archive (tcia): maintaining and operating a public information repository
Kenneth Clark, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, Stanley Phillips, David Maffitt, Michael Pringle, et al · 2013
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Adaptive active learning for image classification
Xin Li and Yuhong Guo · 2013
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Active learning of hyperparameters: An expected cross entropy criterion for active model selection
Johannes Kulick, Robert Lieck, Marc Toussaint, et al · 2014
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, T Langerak, and Arno Klein · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
Holger R Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu, Evrim B Turkbey, and Ronald M Summers · 2015
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Semantic segmentation with modified deep residual networks
Xinze Chen, Guangliang Cheng, Yinghao Cai, Dayong Wen, and Heping Li · 2016
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The image and data archive at the laboratory of neuro imaging
Karen L Crawford, Scott C Neu, and Arthur W Toga · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016
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Adapting to artificial intelligence: radiologists and pathologists as information specialists
Saurabh Jha and Eric J Topol · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Multi-atlas labeling beyond the cranial vault-workshop and challenge
Bennett Landman, Zhoubing Xu, Juan Eugenio Igelsias, Martin Styner, Thomas Robin Langerak, and Arno Klein · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z Chen · 2017
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Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally
Zongwei Zhou, Jae Shin, Lei Zhang, Suryakanth Gurudu, Michael Gotway, and Jianming Liang · 2017
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Automatic multi-organ segmentation on abdominal ct with dense v-networks
Eli Gibson, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve Bandula, Kurinchi Gurusamy, Brian Davidson, Stephen P Pereira, Matthew J Clarkson, and Dean C Barratt · 2018
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Skin lesion analysis towards melanoma detection using deep learning network
Yuexiang Li and Linlin Shen · 2018
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Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, and Mauricio Reyes · 2018
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A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism
Mojtaba Masoudi, Hamid-Reza Pourreza, Mahdi Saadatmand-Tarzjan, Noushin Eftekhari, Fateme Shafiee Zargar, and Masoud Pezeshki Rad · 2018
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Active deep learning with fisher information for patch-wise semantic segmentation
Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Sila Kurugol, and Simon K Warfield · 2018
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Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri
Vanya V Valindria, Nick Pawlowski, Martin Rajchl, Ioannis Lavdas, Eric O Aboagye, Andrea G Rockall, Daniel Rueckert, and Ben Glocker · 2018
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Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Ke Yan, Xiaosong Wang, Le Lu, and Ronald M Summers · 2018
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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, et al · 2019
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The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, et al · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 2019
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Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions
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 · 2019
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Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia
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 · 2019
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Cross-dataset collaborative learning for semantic segmentation
Li Wang, Dong Li, Yousong Zhu, Lu Tian, and Yi Shan · 2021
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Radimagenet: A large-scale radiologic dataset for enhancing deep learning transfer learning research, 2021
Yang Yang, Xueyan Mei, Philip Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Katherine Link, Thomas Yang, Chendi Cao, Ying Wang, Hayit Greenspan, Timothy Deyer, and Zahi Fayad · 2021
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Deep nets: What have they ever done for vision?
Alan L Yuille and Chenxi Liu · 2021
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Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Jianpeng Zhang, Yutong Xie, Yong Xia, and Chunhua Shen · 2021
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Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
Zongwei Zhou · 2021
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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 · 2019
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Convolutional neural networks for radiologic images: a radiologist’s guide
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, and Eyal Klang · 2019
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Intelligent labeling based on fisher information for medical image segmentation using deep learning
Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Xavier Tomas-Fernandez, Sila Kurugol, and Simon K Warfield · 2019
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Integrating active learning and transfer learning for carotid intima-media thickness video interpretation
Zongwei Zhou, Jae Shin, Ruibin Feng, R Todd Hurst, Christopher B Kendall, and Jianming Liang · 2019
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Padchest: A large chest x-ray image dataset with multi-label annotated reports
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 2020
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Deep learning for pancreatic cancer detection: current challenges and future strategies
Linda C Chu and Elliot K Fishman · 2020
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Towards robust medical image segmentation on small-scale data with incomplete labels
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, and Eric P Xing · 2020
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An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in ct imaging., 2020
Nicholas Heller, Sean McSweeney, Matthew Thomas Peterson, Sarah Peterson, Jack Rickman, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Joel Rosenberg, et al · 2020
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Zongwei Zhou, Jae Y Shin, Suryakanth R Gurudu, Michael B Gotway, and Jianming Liang · 2021
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Unetr: Transformers for 3d medical image segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu · 2022
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Synthetic tumors make ai segment tumors better
Qixin Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jie-Neng Chen, Alan Yuille, and Zongwei Zhou · 2022
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Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, et al · 2022
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Fast and low-gpu-memory abdomen ct organ segmentation: the flare challenge
Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Congcong Wang, Fan Zhang, Yu Wang, Yinan Xu, et al · 2022
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Warm start active learning with proxy labels and selection via semi-supervised fine-tuning
Vishwesh Nath, Dong Yang, Holger R Roth, and Daguang Xu · 2022
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Active learning by feature mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Gholamreza Reza Haffari, Anton Van Den Hengel, and Javen Qinfeng Shi · 2022
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Randomized clinical trials of machine learning interventions in health care: a systematic review
Deborah Plana, Dennis L Shung, Alyssa A Grimshaw, Anurag Saraf, Joseph JY Sung, and Benjamin H Kann · 2022
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Self-supervised pre-training of swin transformers for 3d medical image analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh · 2022
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Totalsegmentator: robust segmentation of 104 anatomical structures in ct images
Jakob Wasserthal, Manfred Meyer, Hanns-Christian Breit, Joshy Cyriac, Shan Yang, and Martin Segeroth · 2022
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Toward foundational deep learning models for medical imaging in the new era of transformer networks
Martin J Willemink, Holger R Roth, and Veit Sandfort · 2022
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The felix project: Deep networks to detect pancreatic neoplasms
Yingda Xia, Qihang Yu, Linda Chu, Satomi Kawamoto, Seyoun Park, Fengze Liu, Jieneng Chen, Zhuotun Zhu, Bowen Li, Zongwei Zhou, et al · 2022
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Ai algorithms can assist radiologists in early detection of pancreatic neoplasms through venous and arterial ct imaging
Yingda Xia, Qihang Yu, Linda Chu, Satomi Kawamoto, Seyoun Park, Fengze Liu, Jieneng Chen, Zhuotun Zhu, Bowen Li, Zongwei Zhou, Yongyi Lu, Yan Wang, Wei Shen, Lingxi Xie, Yuyin Zhou, Daniel Fouladi, Shahab Shayesteh, Scott Jefferson Graves, Alejandra Blanco, Eva Zinreich, Ken Kinzler, Ralph Gruban, Bert Vogelstein, Eliot Fishman, and Alan Yuille · 2022
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Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation
Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, and Xinjing Cheng · 2022
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Unsupervised domain adaptation through shape modeling for medical image segmentation
Yuan Yao, Fengze Liu, Zongwei Zhou, Yan Wang, Wei Shen, Alan Yuille, and Yongyi Lu · 2022
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Interpreting medical images
Zongwei Zhou, Michael B Gotway, and Jianming Liang · 2022
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The liver tumor segmentation benchmark (lits)
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