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Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols.
Med3d: Transfer learning for 3d medical image analysis
Sihong Chen, Kai Ma, and Yefeng Zheng · 1904
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Multi-modal volume registration by maximization of mutual information
William M Wells III, Paul Viola, Hideki Atsumi, Shin Nakajima, and Ron Kikinis · 1996
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Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model
Ann B Lee, David Mumford, and Jinggang Huang · 2001
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Nonrigid multimodality image registration
David Mattes, David R Haynor, Hubert Vesselle, Thomas K Lewellyn, and William Eubank · 2001
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Intensity gradient based registration and fusion of multi-modal images
Eldad Haber and Jan Modersitzki · 2006
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Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
Brian B Avants, Charles L Epstein, Murray Grossman, and James C Gee · 2008
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The optimal template effect in hippocampus studies of diseased populations
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Dwi-flair mismatch for the identification of patients with acute ischaemic stroke within 4· 5 h of symptom onset (pre-flair): a multicentre observational study
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Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration
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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
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Towards realtime multimodal fusion for image-guided interventions using self-similarities
Mattias Paul Heinrich, Mark Jenkinson, Bartlomiej W Papież, Sir Michael Brady, and Julia A Schnabel · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
Geert Litjens, Robert Toth, Wendy Van De Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram Van Ginneken, Graham Vincent, Gwenael Guillard, Neil Birbeck, Jindang Zhang, et al · 2014
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scikit-image: image processing in Python
Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, and the scikit-image contributors · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Radiology data from the cancer genome atlas kidney renal clear cell carcinoma TCGA-KIRC collection
O Akin, P Elnajjar, M Heller, R Jarosz, B Erickson, S Kirk, and J Filippini · 2016
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Radiology data from the cancer genome atlas cervical kidney renal papillary cell carcinoma KIRP collection
M Linehan, R Gautam, S Kirk, Y Lee, C Roche, E Bonaccio, and R Jarosz · 2016
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T1-/t2-weighted ratio differs in demyelinated cortex in multiple sclerosis
Kunio Nakamura, Jacqueline T Chen, Daniel Ontaneda, Robert J Fox, and Bruce D Trapp · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Texture analysis as a radiomic marker for differentiating renal tumors
HeiShun Yu, Jonathan Scalera, Maria Khalid, Anne-Sophie Touret, Nicolas Bloch, Baojun Li, Muhammad M Qureshi, Jorge A Soto, and Stephan W Anderson · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Multivariate mixture model for myocardial segmentation combining multi-source images
Xiahai Zhuang · 2018
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data
Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, and N. Sinem Gezer · 2019
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Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease
Pamela J LaMontagne, Tammie LS Benzinger, John C Morris, Sarah Keefe, Russ Hornbeck, Chengjie Xiong, Elizabeth Grant, Jason Hassenstab, Krista Moulder, Andrei G Vlassenko, et al · 2019
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Few labeled atlases are necessary for deep-learning-based segmentation
Hyeon Woo Lee, Mert R Sabuncu, and Adrian V Dalca · 2019
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Nonuniform variational network: deep learning for accelerated nonuniform mr image reconstruction
Jo Schlemper, Seyed Sadegh Mohseni Salehi, Prantik Kundu, Carole Lazarus, Hadrien Dyvorne, Daniel Rueckert, and Michal Sofka · 2019
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Combo loss: Handling input and output imbalance in multi-organ segmentation
Saeid Asgari Taghanaki, Yefeng Zheng, S Kevin Zhou, Bogdan Georgescu, Puneet Sharma, Daguang Xu, Dorin Comaniciu, and Ghassan Hamarneh · 2019
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Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge
Xiahai Zhuang, Lei Li, Christian Payer, Darko Štern, Martin Urschler, Mattias P Heinrich, Julien Oster, Chunliang Wang, Örjan Smedby, Cheng Bian, et al · 2019
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Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 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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CoMIR: Contrastive multimodal image representation for registration
Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, and Natasa Sladoje · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Antsx: A dynamic ecosystem for quantitative biological and medical imaging
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
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Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier
Yutong Xie, Jianpeng Zhang, Yong Xia, and Qi Wu · 2022
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Universeg: Universal medical image segmentation
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R Sabuncu, John Guttag, and Adrian V Dalca · 2023
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Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2023
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Contrast invariant feature representations for segmentation and registration of medical images
Yue Zhi Russ Chua and Adrian V Dalca · 2023
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Nicholas J Tustison, Philip A Cook, Andrew J Holbrook, Hans J Johnson, John Muschelli, Gabriel A Devanyi, Jeffrey T Duda, Sandhitsu R Das, Nicholas C Cullen, Daniel L Gillen, et al · 2020
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Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C Kitamura, Sarthak Pati, et al · 2021
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Learning to see by looking at noise
Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, and Antonio Torralba · 2021
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Deepatrophy: Teaching a neural network to detect progressive changes in longitudinal mri of the hippocampal region in alzheimer’s disease
Mengjin Dong, Long Xie, Sandhitsu R Das, Jiancong Wang, Laura EM Wisse, Robin DeFlores, David A Wolk, Paul A Yushkevich, Alzheimer’s Disease Neuroimaging Initiative, et al · 2021
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Alessa Hering, Lasse Hansen, Tony CW Mok, Albert Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, et al · 2021
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Synthmorph: learning contrast-invariant registration without acquired images
Malte Hoffmann, Benjamin Billot, Douglas N Greve, Juan Eugenio Iglesias, Bruce Fischl, and Adrian V Dalca · 2021
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Hypermorph: Amortized hyperparameter learning for image registration
Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John Guttag, and Adrian V Dalca · 2021
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Scaling laws of synthetic images for model training… for now
Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian · 2023
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Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability
Shangqi Gao, Hangqi Zhou, Yibo Gao, and Xiahai Zhuang · 2023
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Cortical analysis of heterogeneous clinical brain mri scans for large-scale neuroimaging studies
Karthik Gopinath, Douglas N Greve, Sudeshna Das, Steve Arnold, Colin Magdamo, and Juan Eugenio Iglesias · 2023
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Synthsr: A public ai tool to turn heterogeneous clinical brain scans into high-resolution t1-weighted images for 3d morphometry
Juan E Iglesias, Benjamin Billot, Yaël Balbastre, Colin Magdamo, Steven E Arnold, Sudeshna Das, Brian L Edlow, Daniel C Alexander, Polina Golland, and Bruce Fischl · 2023
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A ready-to-use machine learning tool for symmetric multi-modality registration of brain mri
Juan Eugenio Iglesias · 2023
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Dreamteacher: Pretraining image backbones with deep generative models
Daiqing Li, Huan Ling, Amlan Kar, David Acuna, Seung Wook Kim, Karsten Kreis, Antonio Torralba, and Sanja Fidler · 2023
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Segment anything in medical images
Jun Ma and Bo Wang · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Primitive geometry segment pre-training for 3d medical image segmentation
Ryu Tadokoro, Ryosuke Yamada, Kodai Nakashima, Ryo Nakamura, and Hirokatsu Kataoka · 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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Scribbleprompt: Fast and flexible interactive segmentation for any medical image
Hallee E Wong, Marianne Rakic, John Guttag, and Adrian V Dalca · 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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Do vision foundation models enhance domain generalization in medical image segmentation?
Kerem Cekmeceli, Meva Himmetoglu, Guney I Tombak, Anna Susmelj, Ertunc Erdil, and Ender Konukoglu · 2024
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Towards a general-purpose foundation model for computational pathology
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A label-free and data-free training strategy for vasculature segmentation in serial sectioning oct data
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Anystar: Domain randomized universal star-convex 3d instance segmentation
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Developing generalist foundation models from a multimodal dataset for 3d computed tomography, 2024
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