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Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging.
Accuracy of CT colonography for detection of large adenomas and cancers
C Daniel Johnson, Mei-Hsiu Chen, Alicia Y Toledano, Jay P Heiken, Abraham Dachman, Mark D Kuo, Christine O Menias, Betina Siewert, Jugesh I Cheema, Richard G Obregon, et al · 2008
Earlier work this paper cites.
Accuracy of CT colonography for detection of large adenomas and cancers
C Daniel Johnson, Mei-Hsiu Chen, Alicia Y Toledano, Jay P Heiken, Abraham Dachman, Mark D Kuo, Christine O Menias, Betina Siewert, Jugesh I Cheema, Richard G Obregon, et al · 2008
Earlier work this paper cites.
Reduced lung-cancer mortality with low-dose computed tomographic screening
National Lung Screening Trial Research Team · 2011
Earlier work this paper cites.
Reduced lung-cancer mortality with low-dose computed tomographic screening
National Lung Screening Trial Research Team · 2011
Earlier work this paper cites.
SLIC superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
Earlier work this paper cites.
SLIC superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Data from LIDC-IDRI [data set]. The Cancer Imaging Archive, 2015
SG Armato III, G McLennan, L Bidaut, MF McNitt-Gray, CR Meyer, AP Reeves, B Zhao, DR Aberle, CI Henschke, EA Hoffman, et al · 2015
Earlier work this paper cites.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, Thomas Langerak, and Arno Klein · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Data from LIDC-IDRI [data set]. The Cancer Imaging Archive, 2015
SG Armato III, G McLennan, L Bidaut, MF McNitt-Gray, CR Meyer, AP Reeves, B Zhao, DR Aberle, CI Henschke, EA Hoffman, et al · 2015
Earlier work this paper cites.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, Thomas Langerak, and Arno Klein · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Data from pancreas-CT (version 2)[data set]. The Cancer Imaging Archive (2016), 2016
H Roth, A Farag, EB Turkbey, L Lu, J Liu, and RM Summers · 2016
Earlier work this paper cites.
Data from pancreas-CT (version 2)[data set]. The Cancer Imaging Archive (2016), 2016
H Roth, A Farag, EB Turkbey, L Lu, J Liu, and RM Summers · 2016
Earlier work this paper cites.
3D MRI brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2018
Earlier work this paper cites.
3D MRI brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2018
Earlier work this paper cites.
Data from C4KC-KITS [data set]
N Heller, N Sathianathen, A Kalapara, E Walczak, K Moore, H Kaluzniak, J Rosenberg, P Blake, Z Rengel, M Oestreich, et al · 2019
Earlier work this paper cites.
A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization
Ali Morshid, Khaled M Elsayes, Ahmed M Khalaf, Mohab M Elmohr, Justin Yu, Ahmed O Kaseb, Manal Hassan, Armeen Mahvash, Zhihui Wang, John D Hazle, et al · 2019
Earlier work this paper cites.
Data from C4KC-KITS [data set]
N Heller, N Sathianathen, A Kalapara, E Walczak, K Moore, H Kaluzniak, J Rosenberg, P Blake, Z Rengel, M Oestreich, et al · 2019
Earlier work this paper cites.
A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization
Ali Morshid, Khaled M Elsayes, Ahmed M Khalaf, Mohab M Elmohr, Justin Yu, Ahmed O Kaseb, Manal Hassan, Armeen Mahvash, Zhihui Wang, John D Hazle, et al · 2019
Earlier work this paper cites.
Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets
Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, et al · 2020
Earlier work this paper cites.
CT-ORG, a new dataset for multiple organ segmentation in computed tomography
Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin · 2020
Earlier work this paper cites.
Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets
Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, et al · 2020
Earlier work this paper cites.
CT-ORG, a new dataset for multiple organ segmentation in computed tomography
Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin · 2020
Earlier work this paper cites.
Dints: Differentiable neural network topology search for 3d medical image segmentation
Yufan He, Dong Yang, Holger Roth, Can Zhao, and Daguang Xu · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexander Kolesnikov, Alexey Dosovitskiy, Dirk Weissenborn, Georg Heigold, Jakob Uszkoreit, Lucas Beyer, Matthias Minderer, Mostafa Dehghani, Neil Houlsby, Sylvain Gelly, Thomas Unterthiner, and Xiaohua Zhai · 2021
Earlier work this paper cites.
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, et al · 2021
Earlier work this paper cites.
Stony Brook university COVID-19 positive cases
Joel Saltz, Mary Saltz, Prateek Prasanna, Richard Moffitt, Janos Hajagos, Erich Bremer, Joseph Balsamo, and Tahsin Kurc · 2021
Earlier work this paper cites.
Verse: A vertebrae labelling and segmentation benchmark for multi-detector CT images
Anjany Sekuboyina, Malek E Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, et al · 2021
Earlier work this paper cites.
Dints: Differentiable neural network topology search for 3d medical image segmentation
Yufan He, Dong Yang, Holger Roth, Can Zhao, and Daguang Xu · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexander Kolesnikov, Alexey Dosovitskiy, Dirk Weissenborn, Georg Heigold, Jakob Uszkoreit, Lucas Beyer, Matthias Minderer, Mostafa Dehghani, Neil Houlsby, Sylvain Gelly, Thomas Unterthiner, and Xiaohua Zhai · 2021
Earlier work this paper cites.
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, et al · 2021
Earlier work this paper cites.
Stony Brook university COVID-19 positive cases
Joel Saltz, Mary Saltz, Prateek Prasanna, Richard Moffitt, Janos Hajagos, Erich Bremer, Joseph Balsamo, and Tahsin Kurc · 2021
Earlier work this paper cites.
Verse: A vertebrae labelling and segmentation benchmark for multi-detector CT images
Anjany Sekuboyina, Malek E Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, et al · 2021
Earlier work this paper cites.
The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
Earlier work this paper cites.
Monai: An open-source framework for deep learning in healthcare
M Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, et al · 2022
Earlier work this paper cites.
Focalclick: Towards practical interactive image segmentation
Xi Chen, Zhiyan Zhao, Yilei Zhang, Manni Duan, Donglian Qi, and Hengshuang Zhao · 2022
Earlier work this paper cites.
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
Cited alongside, same era.
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
Cited alongside, same era.
Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image
Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang · 2022
Cited alongside, same era.
Deep learning-based segmentation of the thorax in mouse micro-ct scans
Justin Malimban, Danny Lathouwers, Haibin Qian, Frank Verhaegen, Julia Wiedemann, Sytze Brandenburg, and Marius Staring · 2022
Cited alongside, same era.
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
SwinUNETR-V2: Stronger swin transformers with stagewise convolutions for 3D medical image segmentation
Yufan He, Vishwesh Nath, Dong Yang, Yucheng Tang, Andriy Myronenko, and Daguang Xu · 2023
Later among the works it cites.
Continual segment: Towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in ct scans
Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, et al · 2023
Later among the works it cites.
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
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Clip-driven universal model for organ segmentation and tumor detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou · 2023
Later among the works it cites.
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Cited alongside, same era.
The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
Cited alongside, same era.
Monai: An open-source framework for deep learning in healthcare
M Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, et al · 2022
Cited alongside, same era.
Focalclick: Towards practical interactive image segmentation
Xi Chen, Zhiyan Zhao, Yilei Zhang, Manni Duan, Donglian Qi, and Hengshuang Zhao · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image
Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang · 2022
Cited alongside, same era.
Deep learning-based segmentation of the thorax in mouse micro-ct scans
Justin Malimban, Danny Lathouwers, Haibin Qian, Frank Verhaegen, Julia Wiedemann, Sytze Brandenburg, and Marius Staring · 2022
Cited alongside, same era.
Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, et al · 2023
Later among the works it cites.
Voxel-level segmentation of pathologically-proven adrenocortical carcinoma with Ki-67 expression (Adrenal-ACC-Ki67-Seg)[data set]
AW Moawad, AA Ahmed, et al · 2023
Later among the works it cites.
Aorta segmentation from 3d ct in miccai seg. a. 2023 challenge
Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu · 2023
Later among the works it cites.
Automated 3d segmentation of kidneys and tumors in miccai kits 2023 challenge
Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu · 2023
Later among the works it cites.
Automated 3D segmentation of kidneys and tumors in MICCAI KiTS 2023 challenge
Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu · 2023
Later among the works it cites.
Auto3DSeg for brain tumor segmentation from 3D MRI in BraTS 2023 challenge
Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu · 2023
Later among the works it cites.
Aorta segmentation from 3D CT in MICCAI SEG.A. 2023 challenge
Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu · 2023
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AeroPath: An airway segmentation benchmark dataset with challenging pathology
Karen-Helene Støverud, David Bouget, Andre Pedersen, Håkon Olav Leira, Thomas Langø, and Erlend Fagertun Hofstad · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Medical sam adapter: Adapting segment anything model for medical image segmentation
Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin · 2023
Later among the works it cites.
One model to rule them all: Towards universal segmentation for medical images with text prompts
Ziheng Zhao, Yao Zhang, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
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Sam3d: Segment anything model in volumetric medical images
Nhat-Tan Bui, Dinh-Hieu Hoang, Minh-Triet Tran, Gianfranco Doretto, Donald Adjeroh, Brijesh Patel, Arabinda Choudhary, and Ngan Le · 2024
Closest in time.
Reuben Dorent, Roya Khajavi, Tagwa Idris, Erik Ziegler, Bhanusupriya Somarouthu, Heather Jacene, Ann LaCasce, Jonathan Deissler, Jan Ehrhardt, Sofija Engelson, et al · 2024
Closest in time.
Maisi: Medical ai for synthetic imaging
Pengfei Guo, Can Zhao, Dong Yang, Ziyue Xu, Vishwesh Nath, Yucheng Tang, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, et al · 2024
Closest in time.
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
Closest in time.
Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang · 2024
Closest in time.
Multi-center fetal brain tissue annotation (feta) challenge 2022 results
Kelly Payette, Céline Steger, Roxane Licandro, Priscille de Dumast, Hongwei Bran Li, Matthew Barkovich, Liu Li, Maik Dannecker, Chen Chen, Cheng Ouyang, et al · 2024
Closest in time.
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
Closest in time.
Medical vision generalist: Unifying medical imaging tasks in context
Sucheng Ren, Xiaoke Huang, Xianhang Li, Junfei Xiao, Jieru Mei, Zeyu Wang, Alan Yuille, and Yuyin Zhou · 2024
Closest in time.
Segicl: A universal in-context learning framework for enhanced segmentation in medical imaging
Lingdong Shen, Fangxin Shang, Yehui Yang, Xiaoshuang Huang, and Shining Xiang · 2024
Closest in time.
Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases
Amber L Simpson, Jacob Peoples, John M Creasy, Gabor Fichtinger, Natalie Gangai, Krishna N Keshavamurthy, Andras Lasso, Jinru Shia, Michael I D’Angelica, and Richard KG Do · 2024
Closest in time.
Multi-dataset approach to medical image segmentation: Multitalent
Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, and Klaus H Maier-Hein · 2024
Closest in time.
One-prompt to segment all medical images
Junde Wu and Min Xu · 2024
Closest in time.
Sam3d: Segment anything model in volumetric medical images
Nhat-Tan Bui, Dinh-Hieu Hoang, Minh-Triet Tran, Gianfranco Doretto, Donald Adjeroh, Brijesh Patel, Arabinda Choudhary, and Ngan Le · 2024
Closest in time.
Reuben Dorent, Roya Khajavi, Tagwa Idris, Erik Ziegler, Bhanusupriya Somarouthu, Heather Jacene, Ann LaCasce, Jonathan Deissler, Jan Ehrhardt, Sofija Engelson, et al · 2024
Closest in time.
Maisi: Medical ai for synthetic imaging
Pengfei Guo, Can Zhao, Dong Yang, Ziyue Xu, Vishwesh Nath, Yucheng Tang, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, et al · 2024
Closest in time.
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
Closest in time.
Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang · 2024
Closest in time.
Multi-center fetal brain tissue annotation (feta) challenge 2022 results
Kelly Payette, Céline Steger, Roxane Licandro, Priscille de Dumast, Hongwei Bran Li, Matthew Barkovich, Liu Li, Maik Dannecker, Chen Chen, Cheng Ouyang, et al · 2024
Closest in time.
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
Closest in time.
Medical vision generalist: Unifying medical imaging tasks in context
Sucheng Ren, Xiaoke Huang, Xianhang Li, Junfei Xiao, Jieru Mei, Zeyu Wang, Alan Yuille, and Yuyin Zhou · 2024
Closest in time.
Segicl: A universal in-context learning framework for enhanced segmentation in medical imaging
Lingdong Shen, Fangxin Shang, Yehui Yang, Xiaoshuang Huang, and Shining Xiang · 2024
Closest in time.
Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases
Amber L Simpson, Jacob Peoples, John M Creasy, Gabor Fichtinger, Natalie Gangai, Krishna N Keshavamurthy, Andras Lasso, Jinru Shia, Michael I D’Angelica, and Richard KG Do · 2024
Closest in time.
Multi-dataset approach to medical image segmentation: Multitalent
Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, and Klaus H Maier-Hein · 2024
Closest in time.
One-prompt to segment all medical images
Junde Wu and Min Xu · 2024
Closest in time.