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Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis.
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Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes
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Making the most of text semantics to improve biomedical vision–language processing
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Philip Müller, Georgios Kaissis, Congyu Zou, and Daniel Rueckert · 2022
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Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning
Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P Langlotz, Andrew Y Ng, and Pranav Rajpurkar · 2022
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Radiologist productivity analytics: factors impacting abdominal pelvic ct exam reporting times
Amar Udare, Minu Agarwal, Kiret Dhindsa, Amer Alaref, Michael Patlas, Abdullah Alabousi, Yoan K Kagoma, and Christian B van der Pol · 2022
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Contrastive learning of medical visual representations from paired images and text
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz · 2022
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Learning to exploit temporal structure for biomedical vision-language processing
Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Perez-Garcia, Maximilian Ilse, Daniel C Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, et al · 2023
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Towards unifying medical vision-and-language pre-training via soft prompts
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Fan Bai, Yuxin Du, Tiejun Huang, Max Q. H. Meng, and Bo Zhao · 2024
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Merlin: A vision language foundation model for 3d computed tomography
Louis Blankemeier, Joseph Paul Cohen, Ashwin Kumar, Dave Van Veen, Syed Jamal Safdar Gardezi, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Cesar Truyts, et al · 2024
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Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models
Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony CW Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, and Ling Zhang · 2024
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Vision–language foundation model for echocardiogram interpretation
Matthew Christensen, Milos Vukadinovic, Neal Yuan, and David Ouyang · 2024
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Prior: Prototype representation from medical images and reports
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A visual–language foundation model for pathology image analysis using medical twitter
Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J Montine, and James Zou · 2023
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Pmc-clip: Contrastive language-image pre-training using biomedical documents
Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
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Sam-med3d, 2023
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Totalsegmentator: robust segmentation of 104 anatomic structures in ct images
Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al · 2023
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Advancing radiograph representation learning with masked record modeling
Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu · 2023
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Anatomical structure-guided medical vision-language pre-training
Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, and Shujun Wang
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A visual-language foundation model for computational pathology
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Green: Generative radiology report evaluation and error notation
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Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology
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Pairaug: What can augmented image-text pairs do for radiology?
Yutong Xie, Qi Chen, Sinuo Wang, Minh-Son To, Iris Lee, Ee Win Khoo, Kerolos Hendy, Daniel Koh, Yong Xia, and Qi Wu · 2024
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