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Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning.
Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation
Peilun Shi, Jianing Qiu, Sai Mu Dalike Abaxi, Hao Wei, Frank P-W Lo, and Wu Yuan · 1947
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Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma · 2014
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The multimodal brain tumor image segmentation benchmark (brats)
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, 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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Original multi-parametric mri images of prostate
Guillaume Lemaitre, Robert Martí Marly, and Fabrice Meriaudeau · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Focal loss for dense object detection
T-YLPG Ross and GKHP Dollár · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification
Mugahed A Al-Antari, Mohammed A Al-Masni, Mun-Taek Choi, Seung-Moo Han, and Tae-Seong Kim · 2018
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Prostatex challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images
Samuel G Armato III, Henkjan Huisman, Karen Drukker, Lubomir Hadjiiski, Justin S Kirby, Nicholas Petrick, George Redmond, Maryellen L Giger, Kenny Cha, Artem Mamonov, et al · 2018
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Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al · 2018
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Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet
Nicholas Bien, Pranav Rajpurkar, Robyn L Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, et al · 2018
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Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
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A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features
Ashirbani Saha, Michael R Harowicz, Lars J Grimm, Connie E Kim, Sujata V Ghate, Ruth Walsh, and Maciej A Mazurowski · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Deep learning approaches for data augmentation and classification of breast masses using ultrasound images
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Fahmy Aly · 2019
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Brain mri segmentation: Brain mri images together with manual flair abnormality segmentation masks
Mateusz Buda · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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A multi-organ nucleus segmentation challenge
Neeraj Kumar, Ruchika Verma, Deepak Anand, Yanning Zhou, Omer Fahri Onder, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng, Jiahui Li, Zhiqiang Hu, et al · 2019
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2018 robotic scene segmentation challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, et al · 2020
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Tinytl: Reduce memory, not parameters for efficient on-device learning
Han Cai, Chuang Gan, Ligeng Zhu, and Song Han · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Xi Fang and Pingkun Yan · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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Kvasir-seg: A segmented polyp dataset
Debesh Jha, Pia H Smedsrud, Michael A Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D Johansen · 2020
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Extracting lungs from ct images via deep convolutional neural network based segmentation and two-pass contour refinement
Caixia Liu and Mingyong Pang · 2020
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Contrastive learning for unpaired image-to-image translation
Taesung Park, Alexei A Efros, Richard Zhang, and Jun-Yan Zhu · 2020
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Episurg: a dataset of postoperative magnetic resonance images (mri) for quantitative analysis of resection neurosurgery for refractory epilepsy. university college london
F Pérez-García, R Rodionov, A Alim-Marvasti, R Sparks, J Duncan, and S Ourselin · 2020
Cited alongside, same era.
Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network
Alexander J Daniel, Charlotte E Buchanan, Thomas Allcock, Daniel Scerri, Eleanor F Cox, Benjamin L Prestwich, and Susan T Francis · 2021
Cited alongside, same era.
X-ray images of the hip joints
Daniel Gut · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
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
On the effectiveness of parameter-efficient fine-tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier · 2023
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Desam: Decoupling segment anything model for generalizable medical image segmentation
Yifan Gao, Wei Xia, Dingdu Hu, and Xin Gao · 2023
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3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable medical image segmentation
Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, and Qi Dou · 2023
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Cellvit: Vision transformers for precise cell segmentation and classification
Fabian Hörst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens Siveke, Barbara Grünwald, Jan Egger, et al · 2023
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Cited alongside, same era.
Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation
A Emre Kavur, N Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, et al · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment
Francesco Marzola, Nens van Alfen, Jonne Doorduin, and Kristen M Meiburger · 2021
Cited alongside, same era.
U-net and its variants for medical image segmentation: A review of theory and applications
Nahian Siddique, Sidike Paheding, Colin P Elkin, and Vijay Devabhaktuni · 2021
Cited alongside, same era.
Sub-cortical structure segmentation database for young population
Jayanthi Sivaswamy, Alphin J Thottupattu, Raghav Mehta, R Sheelakumari, Chandrasekharan Kesavadas, et al · 2021
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
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.
Yankai Jiang, Mingze Sun, Heng Guo, Xiaoyu Bai, Ke Yan, Le Lu, and Minfeng Xu · 2023
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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
Later among the works it cites.
Xian Lin, Yangyang Xiang, Li Zhang, Xin Yang, Zengqiang Yan, and Li Yu · 2023
Later among the works it cites.
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
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Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation
Jay N Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder, S Swaroop Vedula, and Vishal M Patel · 2023
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Zhongxi Qiu, Yan Hu, Heng Li, and Jiang Liu · 2023
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Autosam: Adapting sam to medical images by overloading the prompt encoder, 2023
Tal Shaharabany, Aviad Dahan, Raja Giryes, and Lior Wolf · 2023
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Lumbar spine segmentation in mr images: a dataset and a public benchmark
Jasper W van der Graaf, Miranda L van Hooff, Constantinus FM Buckens, Matthieu Rutten, Job LC van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, and Nikolas Lessmann · 2023
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Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset
Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, Jichen Yang, Lisa M Ho, Brian C Allen, Wendy L Ehieli, Chad M Miller, Jikai Zhang, and Maciej A Mazurowski · 2023
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Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang · 2023
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Surgicalsam: Efficient class promptable surgical instrument segmentation
Wenxi Yue, Jing Zhang, Kun Hu, Yong Xia, Jiebo Luo, and Zhiyong Wang · 2023
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Customized segment anything model for medical image segmentation
Kaidong Zhang and Dong Liu · 2023
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Towards segment anything model (sam) for medical image segmentation: a survey
Yichi Zhang and Rushi Jiao · 2023
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Self pre-training with masked autoencoders for medical image classification and segmentation
Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, and Prateek Prasanna · 2023
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Advancing volumetric medical image segmentation via global-local masked autoencoder
Jia-Xin Zhuang, Luyang Luo, and Hao Chen · 2023
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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
Closest in time.
Segmentanybone: A universal model that segments any bone at any location on mri, 2024
Hanxue Gu, Roy Colglazier, Haoyu Dong, Jikai Zhang, Yaqian Chen, Zafer Yildiz, Yuwen Chen, Lin Li, Jichen Yang, Jay Willhite, Alex M. Meyer, Brian Guo, Yashvi Atul Shah, Emily Luo, Shipra Rajput, Sally Kuehn, Clark Bulleit, Kevin A. Wu, Jisoo Lee, Brandon Ramirez, Darui Lu, Jay M. Levin, and Maciej A. Mazurowski · 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
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The effect of intrinsic dataset properties on generalization: Unraveling learning differences between natural and medical images
Nicholas Konz and Maciej A Mazurowski · 2024
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Foundation models for biomedical image segmentation: A survey
Ho Hin Lee, Yu Gu, Theodore Zhao, Yanbo Xu, Jianwei Yang, Naoto Usuyama, Cliff Wong, Mu Wei, Bennett A Landman, Yuankai Huo, et al · 2024
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Self-sampling meta sam: Enhancing few-shot medical image segmentation with meta-learning
Tianang Leng, Yiming Zhang, Kun Han, and Xiaohui Xie · 2024
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A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast mri
Christopher O Lew, Majid Harouni, Ella R Kirksey, Elianne J Kang, Haoyu Dong, Han Gu, Lars J Grimm, Ruth Walsh, Dorothy A Lowell, and Maciej A. Mazurowski · 2024
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Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang · 2024
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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
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Fremim: Fourier transform meets masked image modeling for medical image segmentation
Wenxuan Wang, Jing Wang, Chen Chen, Jianbo Jiao, Yuanxiu Cai, Shanshan Song, and Jiangyun Li · 2024
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Sam fewshot finetuning for anatomical segmentation in medical images
Weiyi Xie, Nathalie Willems, Shubham Patil, Yang Li, and Mayank Kumar · 2024
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