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Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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Interactive segmentation of medical images through fully convolutional neural networks
Sakinis, T.; Milletari, F.; Roth, H.; Korfiatis, P.; Kostandy, P.; Philbrick, K.; Akkus, Z.; Xu, Z.; Xu, D.; and Erickson, B. J. 2019 · 1903
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3D Slicer
Pieper, S.; Halle, M.; and Kikinis, R. 2004 · 2004
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Random features for large-scale kernel machines
Rahimi, A.; and Recht, B. 2007 · 2007
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Interactive segmentation framework of the medical imaging interaction toolkit
Maleike, D.; Nolden, M.; Meinzer, H.-P.; and Wolf, I. 2009 · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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An overview of interactive medical image segmentation
Zhao, F.; and Xie, X. 2013 · 2013
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Landman, B.; Xu, Z.; Igelsias, J.; Styner, M.; Langerak, T.; and Klein, A. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Improving computer-aided detection using convolutional neural networks and random view aggregation
Roth, H. R.; Lu, L.; Liu, J.; Yao, J.; Seff, A.; Cherry, K.; Kim, L.; and Summers, R. M. 2015 · 2015
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3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö.; Abdulkadir, A.; Lienkamp, S. S.; Brox, T.; and Ronneberger, O. 2016 · 2016
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Quantitative dual-energy computed tomography supports a vascular etiology of smoking-induced inflammatory lung disease
Iyer, K. S.; Newell Jr, J. D.; Jin, D.; Fuld, M. K.; Saha, P. K.; Hansdottir, S.; and Hoffman, E. A. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Shin, H. C.; Roth, H. R.; Gao, M.; Lu, L.; Xu, Z.; Nogues, I.; Yao, J.; Mollura, D.; and Summers, R. M. 2016 · 2016
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Instance normalization: The missing ingredient for fast stylization
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2016 · 2016
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Deep interactive object selection
Xu, N.; Price, B.; Cohen, S.; Yang, J.; and Huang, T. S. 2016 · 2016
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ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images
Yushkevich, P. A.; Gao, Y.; and Gerig, G. 2016 · 2016
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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
AlBadawy, E. A.; Saha, A.; and Mazurowski, M. A. 2018 · 2018
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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S.; Ghosh, R.; Tanamala, S.; Biviji, M.; Campeau, N. G.; Venugopal, V. K.; Mahajan, V.; Rao, P.; and Warier, P. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Keypoint transfer for fast whole-body segmentation
Wachinger, C.; Toews, M.; Langs, G.; Wells, W.; and Golland, P. 2018 · 2018
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Interactive medical image segmentation using deep learning with image-specific fine tuning
Wang, G.; Li, W.; Zuluaga, M. A.; Pratt, R.; Patel, P. A.; Aertsen, M.; Doel, T.; David, A. L.; Deprest, J.; Ourselin, S.; et al. 2018 · 2018
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Lymphocyte-driven regional immunopathology in pneumonitis caused by impaired central immune tolerance
Ferré, E. M.; Break, T. J.; Burbelo, P. D.; Allgäuer, M.; et al. 2019 · 2019
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Deep learning techniques for medical image segmentation: achievements and challenges
Hesamian, M. H.; Jia, W.; He, X.; and Kennedy, P. 2019 · 2019
Cited alongside, same era.
Scribble-based hierarchical weakly supervised learning for brain tumor segmentation
Ji, Z.; Shen, Y.; Ma, C.; and Gao, M. 2019 · 2019
Cited alongside, same era.
ImJoy: an open-source computational platform for the deep learning era
Ouyang, W.; Mueller, F.; Hjelmare, M.; Lundberg, E.; and Zimmer, C. 2019 · 2019
Cited alongside, same era.
Semantic image synthesis with spatially-adaptive normalization
Park, T.; Liu, M.-Y.; Wang, T.-C.; and Zhu, J.-Y. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
Cited alongside, same era.
Self-supervised pre-training of swin transformers for 3d medical image analysis
Tang, Y.; Yang, D.; Li, W.; Roth, H. R.; Landman, B.; Xu, D.; Nath, V.; and Hatamizadeh, A. 2022 · 2022
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Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study
Ye, X.; Guo, D.; Ge, J.; Yan, S.; Xin, Y.; Song, Y.; Yan, Y.; Huang, B.-s.; Hung, T.-M.; Zhu, Z.; et al. 2022 · 2022
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Rest v2: simpler, faster and stronger
Zhang, Q.; and Yang, Y.-B. 2022 · 2022
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Cheng, J.; Ye, J.; Deng, Z.; Chen, J.; Li, T.; Wang, H.; Su, Y.; Huang, Z.; Chen, J.; Jiang, L.; Sun, H.; He, J.; Zhang, S.; Zhu, M.; and Qiao, Y. 2023 · 2023
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Deng, R.; Cui, C.; Liu, Q.; Yao, T.; Remedios, L. W.; Bao, S.; Landman, B. A.; Wheless, L. E.; Coburn, L. A.; Wilson, K. T.; et al. 2023 · 2023
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Wang, X.; Zhang, L.; Roth, H.; Xu, D.; and Xu, Z. 2019 · 2019
Cited alongside, same era.
NuClick: a deep learning framework for interactive segmentation of microscopic images
Koohbanani, N. A.; Jahanifar, M.; Tajadin, N. Z.; and Rajpoot, N. 2020 · 2020
Cited alongside, same era.
International evaluation of an AI system for breast cancer screening
McKinney, S. M.; Sieniek, M.; Godbole, V.; Godwin, J.; Antropova, N.; Ashrafian, H.; Back, T.; Chesus, M.; Corrado, G. S.; Darzi, A.; et al. 2020 · 2020
Cited alongside, same era.
Detection of anaemia from retinal fundus images via deep learning
Mitani, A.; Huang, A.; Venugopalan, S.; Corrado, G. S.; Peng, L.; Webster, D. R.; Hammel, N.; Liu, Y.; and Varadarajan, A. V. 2020 · 2020
Cited alongside, same era.
Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M.; Srinivasan, P.; Mildenhall, B.; Fridovich-Keil, S.; Raghavan, N.; Singhal, U.; Ramamoorthi, R.; Barron, J.; and Ng, R. 2020 · 2020
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
Cited alongside, same era.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F.; Jaeger, P. F.; Kohl, S. A.; Petersen, J.; and Maier-Hein, K. H. 2021 · 2021
Cited alongside, same era.
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Accuracy of segment-anything model (sam) in medical image segmentation tasks
He, S.; Bao, R.; Li, J.; Grant, P. E.; and Ou, Y. 2023 · 2023
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Segment Anything
Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.-Y.; Dollar, P.; and Girshick, R. 2023 · 2023
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SimpleClick: Interactive image segmentation with simple vision transformers
Liu, Q.; Xu, Z.; Bertasius, G.; and Niethammer, M. 2023 · 2023
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Ma, J.; Zhang, Y.; Gu, S.; Ge, C.; Ma, S.; Young, A.; Zhu, C.; Meng, K.; Yang, X.; Huang, Z.; et al. 2023 · 2023
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Segment anything model for medical image analysis: an experimental study
Maciej, A.; Haoyu, D.; Hanxue, G.; Jichen, Y.; Nicholas, K.; and Yixin, Z. 2023 · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A.; et al. 2023 · 2023
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SAM. MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Wald, T.; Roy, S.; Koehler, G.; Disch, N.; Rokuss, M. R.; Holzschuh, J.; Zimmerer, D.; and Maier-Hein, K. 2023 · 2023
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Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images
Wasserthal, J.; Breit, H.-C.; Meyer, M. T.; Pradella, M.; Hinck, D.; Sauter, A. W.; Heye, T.; Boll, D. T.; Cyriac, J.; Yang, S.; et al. 2023 · 2023
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Medical sam adapter: Adapting segment anything model for medical image segmentation
Wu, J.; Fu, R.; Fang, H.; Liu, Y.; Wang, Z.; Xu, Y.; Jin, Y.; and Arbel, T. 2023 · 2023
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SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
Yue, W.; Zhang, J.; Hu, K.; Xia, Y.; Luo, J.; and Wang, Z. 2023 · 2023
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Customized segment anything model for medical image segmentation
Zhang, K.; and Liu, D. 2023 · 2023
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nnformer: Volumetric medical image segmentation via a 3d transformer
Zhou, H.-Y.; Guo, J.; Zhang, Y.; Han, X.; Yu, L.; Wang, L.; and Yu, Y. 2023 · 2023
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Segment everything everywhere all at once
Zou, X.; Yang, J.; Zhang, H.; Li, F.; Li, L.; Gao, J.; and Lee, Y. J. 2023 · 2023
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MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation
Chen, C.; Miao, J.; Wu, D.; Zhong, A.; Yan, Z.; Kim, S.; Hu, J.; Liu, Z.; Sun, L.; Li, X.; Liu, T.; Heng, P.-A.; and Li, Q. 2024 · 2024
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SegVol: Universal and Interactive Volumetric Medical Image Segmentation
Du, Y.; Bai, F.; Huang, T.; and Zhao, B. 2024 · 2024
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3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
Gong, S.; Zhong, Y.; Ma, W.; Li, J.; Wang, Z.; Zhang, J.; Heng, P.-A.; and Dou, Q. 2024 · 2024
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Med-query: Steerable parsing of 9-dof medical anatomies with query embedding
Guo, H.; Zhang, J.; Yan, K.; Lu, L.; and Xu, M. 2024 · 2024
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Segment anything model for medical images?
Huang, Y.; Yang, X.; Liu, L.; Zhou, H.; Chang, A.; Zhou, X.; Chen, R.; Yu, J.; Chen, J.; Chen, C.; et al. 2024 · 2024
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Segment anything in medical images
Ma, J.; He, Y.; Li, F.; Han, L.; You, C.; and Wang, B. 2024 · 2024
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