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Positron Emission Tomography (PET) is a powerful molecular imaging tool that plays a crucial role in modern medical diagnostics by visualizing radio-tracer distribution to reveal physiological processes.
A comparative study of medical imaging techniques
Hany Kasban, MAM El-Bendary, and DH Salama · 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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Multi-organ mapping of cancer risk
Liqin Zhu, David Finkelstein, Culian Gao, Lei Shi, Yongdong Wang, Dolores López-Terrada, Kasper Wang, Sarah Utley, Stanley Pounds, Geoffrey Neale, et al · 2016
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
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New machine-learning technologies for computer-aided diagnosis
Charles J. Lynch and Conor Liston · 2018
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Lifex: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity
Christophe Nioche, Fanny Orlhac, Sarah Boughdad, Sylvain Reuzé, Jessica Goya-Outi, Charlotte Robert, Claire Pellot-Barakat, Michael Soussan, Frédérique Frouin, and Irène Buvat · 2018
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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina, Marleen De Bruijne, and Josien PW Pluim · 2019
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3d mri brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2019
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Atlas-based multiorgan segmentation for dynamic abdominal pet
Silin Ren, Priscille Laub, Yihuan Lu, Mika Naganawa, and Richard E Carson · 2019
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Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu · 2021
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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A whole-body fdg-pet/ct dataset with manually annotated tumor lesions
Sergios Gatidis, Tobias Hepp, Marcel Früh, Christian La Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens Cyran, and Daniel Rubin · 2022
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Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji, Haotian Bai, Chongjian Ge, Jie Yang, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhanng, Wanling Ma, Xiang Wan, et al · 2022
Cited alongside, same era.
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, and Xiaoping Yang · 2022
Cited alongside, same era.
Head and neck tumor segmentation in pet/ct: the hecktor challenge
Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, Sarah Boughdad, Hesham Elhalawani, Joel Castelli, Martin Vallières, Simeng Zhu, Juanying Xie, Ying Peng, et al · 2022
Cited alongside, same era.
Identifying the individual metabolic abnormities from a systemic perspective using whole-body pet imaging
Tao Sun, Zhenguo Wang, Yaping Wu, Fengyun Gu, Xiaochen Li, Yan Bai, Chushu Shen, Zhanli Hu, Dong Liang, Xin Liu, et al · 2022
Cited alongside, same era.
Toward foundational deep learning models for medical imaging in the new era of transformer networks
Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks
Jin Ye, Junlong Cheng, Jianpin Chen, Zhongying Deng, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, et al · 2023
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M3d: Advancing 3d medical image analysis with multi-modal large language models
Fan Bai, Yuxin Du, Tiejun Huang, Max Q-H Meng, and Bo Zhao · 2024
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Segvol: Universal and interactive volumetric medical image segmentation
Yuxin Du, Fan Bai, Tiejun Huang, and Bo Zhao · 2024
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3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation
Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, and Qi Dou · 2024
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Segment anything model for medical images?
Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, et al · 2024
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Martin J. Willemink, Holger R. Roth, and Veit Sandfort · 2022
Cited alongside, same era.
Ziyan Huang, Haoyu Wang, Zhongying Deng, Jin Ye, Yanzhou Su, Hui Sun, Junjun He, Yun Gu, Lixu Gu, Shaoting Zhang, et al · 2023
Cited alongside, same era.
Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Rushi Jiao, Yichi Zhang, Le Ding, Bingsen Xue, Jicong Zhang, Rong Cai, and Cheng Jin · 2023
Cited alongside, same era.
Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Cited alongside, same era.
Segment anything model for semi-supervised medical image segmentation via selecting reliable pseudo-labels
Ning Li, Lianjin Xiong, Wei Qiu, Yudong Pan, Yiqian Luo, and Yangsong Zhang · 2023
Cited alongside, same era.
Segment anything model for medical image analysis: an experimental study
Maciej A Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang · 2023
Cited alongside, same era.
Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra F H Abad, Harlan M. Krumholz, Jure Leskovec, Eric J. Topol, and Pranav Rajpurkar · 2023
Cited alongside, same era.
Advances in pet imaging of cancer
Johannes Schwenck, Dominik Sonanini, Jonathan M Cotton, Hans-Georg Rammensee, Christian la Fougère, Lars Zender, and Bernd J Pichler · 2023
Cited alongside, same era.
Later among the works it cites.
Yuyan Shi, Jialu Ma, Jin Yang, Shasha Wang, and Yichi Zhang · 2024
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Sam-med3d: towards general-purpose segmentation models for volumetric medical images
Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, et al · 2024
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Scribbleprompt: fast and flexible interactive segmentation for any biomedical image
Hallee E Wong, Marianne Rakic, John Guttag, and Adrian V Dalca · 2024
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On the challenges and perspectives of foundation models for medical image analysis
Shaoting Zhang and Dimitris Metaxas · 2024
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Semisam: Enhancing semi-supervised medical image segmentation via sam-assisted consistency regularization
Yichi Zhang, Jin Yang, Yuchen Liu, Yuan Cheng, and Yuan Qi · 2024
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nninteractive: Redefining 3d promptable segmentation
Fabian Isensee, Maximilian Rokuss, Lars Krämer, Stefan Dinkelacker, Ashis Ravindran, Florian Stritzke, Benjamin Hamm, Tassilo Wald, Moritz Langenberg, Constantin Ulrich, et al · 2025
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Medlsam: Localize and segment anything model for 3d ct images
Wenhui Lei, Wei Xu, Kang Li, Xiaofan Zhang, and Shaoting Zhang · 2025
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Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body pet imaging: a multi-center and cross-tracer study
Hanzhong Wang, Xiaoya Qiao, Wenxiang Ding, Gaoyu Chen, Ying Miao, Rui Guo, Xiaohua Zhu, Zhaoping Cheng, Jiehua Xu, Biao Li, et al · 2025
Closest in time.