Variability and reproducibility in deep learning for medical image segmentation
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of clas...
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| Vydáno v: | Scientific reports Ročník 10; číslo 1; s. 13724 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group UK
13.08.2020
Nature Publishing Group |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results. |
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| AbstractList | Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results.Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results. Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results. |
| ArticleNumber | 13724 |
| Author | Vuillerme, Nicolas Guedria, Soulaimane Renard, Félix Palma, Noel De |
| Author_xml | – sequence: 1 givenname: Félix surname: Renard fullname: Renard, Félix email: felix.renard@univ-grenoble-alpes.fr organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Univ. Grenoble Alpes, AGEIS – sequence: 2 givenname: Soulaimane surname: Guedria fullname: Guedria, Soulaimane organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Univ. Grenoble Alpes, AGEIS – sequence: 3 givenname: Noel De surname: Palma fullname: Palma, Noel De organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG – sequence: 4 givenname: Nicolas surname: Vuillerme fullname: Vuillerme, Nicolas organization: Univ. Grenoble Alpes, AGEIS, Institut Universitaire de France |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32792540$$D View this record in MEDLINE/PubMed https://hal.science/hal-02917117$$DView record in HAL |
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| Cites_doi | 10.1016/j.compmedimag.2005.12.001 10.1038/srep20280 10.1186/s40537-019-0197-0 10.1038/s41746-019-0079-z 10.1016/j.media.2016.05.004 10.1109/TMI.2014.2377694 10.1109/TMI.2016.2535222 10.1016/S0896-6273(02)00569-X 10.1016/j.media.2016.10.004 10.1109/TMI.2016.2548501 10.1109/TMI.2016.2528821 10.1118/1.4810971 10.1006/jmps.1999.1279 10.1016/j.array.2019.100004 10.1038/nm.3390 10.1016/j.media.2017.07.005 10.1016/j.cviu.2017.04.002 10.1016/j.media.2004.12.004 10.1016/j.jneumeth.2016.10.007 10.1038/s41598-017-05728-9 10.1080/21681163.2016.1182072 10.1016/j.neuroimage.2014.12.061 10.1109/JSTSP.2008.2011119 10.1038/533452a 10.1016/j.zemedi.2018.11.002 10.1038/s41598-017-01779-0 10.1038/nature14539 10.1109/TMI.2007.908121 10.1109/TMI.2016.2538465 10.1016/j.neuroimage.2016.01.024 10.1038/s41598-017-05300-5 10.1186/s12880-015-0068-x 10.1037/0033-2909.86.2.420 10.25080/Majora-8b375195-003 10.1145/2647868.2654889 10.1007/978-3-642-35289-8_3 10.1109/ICDMAI.2017.8073516 10.1109/CVPR.2015.7298965 10.1109/ISBI.2016.7493261 10.1155/2016/8356294 10.1007/978-3-030-23987-9_10 10.1007/978-3-319-46976-8_7 10.1109/TPAMI.2021.3059968 10.1145/2733373.2807412 10.1109/ISBI.2016.7493515 10.1007/978-3-319-10470-6_39 10.1007/978-3-319-24574-4_28 10.1109/CVPRW.2015.7301312 10.1007/978-3-319-46723-8_54 10.5281/zenodo.27878 10.1007/978-3-319-46976-8_15 10.1109/CVPR.2018.00907 |
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| References | (CR20) 2019 CR39 Bao, Chung (CR70) 2018; 6 CR38 CR37 CR36 Milletari (CR57) 2017; 164 Fisher (CR24) 2006 CR35 Shorten, Khoshgoftaar (CR27) 2019; 6 CR34 CR33 CR77 CR32 CR31 CR75 CR30 CR73 CR71 Shrout, Fleiss (CR23) 1979; 86 Zhou, Ruan, Canu (CR45) 2019; 3 Kleesiek (CR72) 2016; 129 CR49 CR44 CR43 Brosch (CR52) 2016; 35 CR41 Lundervold, Lundervold (CR46) 2019; 29 CR40 Chen, Xu, Yang, Egger (CR12) 2016; 6 Gulli, Pal (CR63) 2017 Kamnitsas (CR47) 2017; 36 Taha, Hanbury (CR19) 2015; 15 Browne (CR25) 2000; 44 Silveira (CR5) 2009; 3 Baker (CR21) 2016; 533 Pereira, Pinto, Alves, Silva (CR48) 2016; 35 Zhang (CR65) 2015; 108 Fortunati (CR11) 2013; 40 LeCun, Bengio, Hinton (CR14) 2015; 521 Mezer (CR3) 2013; 19 CR18 CR17 Sharma (CR4) 2017; 7 CR16 Sharma, Aggarwal (CR2) 2010; 35 Trebeschi (CR8) 2017; 7 CR59 CR53 Goodfellow, Bengio, Courville (CR13) 2016 CR51 CR50 Chrástek (CR6) 2005; 9 Ben-Nun, Hoefler (CR42) 2019; 52 Withey, Koles (CR1) 2008; 10 Moeskops (CR56) 2016; 35 Tu (CR10) 2008; 27 Litjens (CR15) 2017; 42 Stupple, Singerman, Celi (CR22) 2019; 2 Fischl (CR9) 2002; 33 CR29 CR28 Piater, Cohen, Zhang, Atighetchi (CR74) 1998; 98 CR69 Ghafoorian (CR7) 2017; 7 CR67 CR66 CR64 Havaei (CR55) 2017; 35 Udupa (CR26) 2006; 30 CR62 CR61 CR60 Demšar (CR76) 2006; 7 Choi, Jin (CR68) 2016; 274 Mansoor (CR58) 2016; 35 Menze (CR54) 2014; 34 69920_CR53 JH Piater (69920_CR74) 1998; 98 MW Browne (69920_CR25) 2000; 44 K Sharma (69920_CR4) 2017; 7 W Zhang (69920_CR65) 2015; 108 G Litjens (69920_CR15) 2017; 42 69920_CR59 Y LeCun (69920_CR14) 2015; 521 69920_CR16 T Brosch (69920_CR52) 2016; 35 H Choi (69920_CR68) 2016; 274 69920_CR50 69920_CR51 A Gulli (69920_CR63) 2017 DJ Withey (69920_CR1) 2008; 10 P Moeskops (69920_CR56) 2016; 35 X Chen (69920_CR12) 2016; 6 T Ben-Nun (69920_CR42) 2019; 52 V Fortunati (69920_CR11) 2013; 40 69920_CR64 69920_CR66 AA Taha (69920_CR19) 2015; 15 S Pereira (69920_CR48) 2016; 35 69920_CR67 M Ghafoorian (69920_CR7) 2017; 7 M Silveira (69920_CR5) 2009; 3 69920_CR69 N Sharma (69920_CR2) 2010; 35 69920_CR60 69920_CR61 JK Udupa (69920_CR26) 2006; 30 69920_CR62 I Goodfellow (69920_CR13) 2016 R Chrástek (69920_CR6) 2005; 9 J Demšar (69920_CR76) 2006; 7 69920_CR17 C Shorten (69920_CR27) 2019; 6 K Kamnitsas (69920_CR47) 2017; 36 69920_CR18 A Stupple (69920_CR22) 2019; 2 BH Menze (69920_CR54) 2014; 34 69920_CR31 69920_CR75 69920_CR32 69920_CR33 69920_CR77 69920_CR34 69920_CR35 69920_CR36 69920_CR37 69920_CR38 M Havaei (69920_CR55) 2017; 35 69920_CR71 PE Shrout (69920_CR23) 1979; 86 69920_CR73 69920_CR30 A Mansoor (69920_CR58) 2016; 35 S Trebeschi (69920_CR8) 2017; 7 AS Lundervold (69920_CR46) 2019; 29 A Mezer (69920_CR3) 2013; 19 69920_CR28 69920_CR29 Z Tu (69920_CR10) 2008; 27 69920_CR43 69920_CR44 69920_CR49 F Milletari (69920_CR57) 2017; 164 M Baker (69920_CR21) 2016; 533 B Fischl (69920_CR9) 2002; 33 69920_CR40 69920_CR41 RA Fisher (69920_CR24) 2006 S Bao (69920_CR70) 2018; 6 T Zhou (69920_CR45) 2019; 3 69920_CR39 National Academies of Sciences, Engineering, and Medicine (69920_CR20) 2019 J Kleesiek (69920_CR72) 2016; 129 |
| References_xml | – volume: 30 start-page: 75 year: 2006 end-page: 87 ident: CR26 article-title: A framework for evaluating image segmentation algorithms publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2005.12.001 – ident: CR49 – volume: 6 start-page: 20280 year: 2016 ident: CR12 article-title: A semi-automatic computer-aided method for surgical template design publication-title: Sci. Rep. doi: 10.1038/srep20280 – volume: 6 start-page: 60 year: 2019 ident: CR27 article-title: A survey on image data augmentation for deep learning publication-title: J. Big Data doi: 10.1186/s40537-019-0197-0 – ident: CR39 – ident: CR16 – ident: CR51 – volume: 2 start-page: 2 year: 2019 ident: CR22 article-title: The reproducibility crisis in the age of digital medicine publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0079-z – ident: CR35 – ident: CR29 – ident: CR61 – ident: CR77 – volume: 35 start-page: 18 year: 2017 end-page: 31 ident: CR55 article-title: Brain tumor segmentation with deep neural networks publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.05.004 – year: 2016 ident: CR13 publication-title: Deep Learning – year: 2017 ident: CR63 publication-title: Deep Learning with Keras – ident: CR71 – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: CR76 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 34 start-page: 1993 year: 2014 end-page: 2024 ident: CR54 article-title: The multimodal brain tumor image segmentation benchmark (brats) publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – ident: CR67 – ident: CR75 – volume: 52 start-page: 65 year: 2019 ident: CR42 article-title: Demystifying parallel and distributed deep learning: an in-depth concurrency analysis publication-title: ACM Comput. Surv. (CSUR) – ident: CR50 – volume: 35 start-page: 1856 year: 2016 end-page: 1865 ident: CR58 article-title: Deep learning guided partitioned shape model for anterior visual pathway segmentation publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2535222 – ident: CR32 – ident: CR60 – ident: CR36 – volume: 33 start-page: 341 year: 2002 end-page: 355 ident: CR9 article-title: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain publication-title: Neuron doi: 10.1016/S0896-6273(02)00569-X – ident: CR64 – volume: 36 start-page: 61 year: 2017 end-page: 78 ident: CR47 article-title: Efficient multi-scale 3d CNN with fully connected crf for accurate brain lesion segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – volume: 35 start-page: 1252 year: 2016 end-page: 1261 ident: CR56 article-title: Automatic segmentation of mr brain images with a convolutional neural network publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2548501 – volume: 10 start-page: 125 year: 2008 end-page: 148 ident: CR1 article-title: A review of medical image segmentation: methods and available software publication-title: Int. J. Bioelectromagn. – ident: CR18 – ident: CR43 – volume: 35 start-page: 1229 year: 2016 end-page: 1239 ident: CR52 article-title: Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528821 – ident: CR66 – volume: 40 start-page: 071905 year: 2013 ident: CR11 article-title: Tissue segmentation of head and neck ct images for treatment planning: a multiatlas approach combined with intensity modeling publication-title: Med. Phys. doi: 10.1118/1.4810971 – volume: 44 start-page: 108 year: 2000 end-page: 132 ident: CR25 article-title: Cross-validation methods publication-title: J. Math. Psychol. doi: 10.1006/jmps.1999.1279 – volume: 3 start-page: 100004 year: 2019 ident: CR45 article-title: A review: deep learning for medical image segmentation using multi-modality fusion publication-title: Array doi: 10.1016/j.array.2019.100004 – volume: 19 start-page: 1667 year: 2013 ident: CR3 article-title: Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging publication-title: Nat. Med. doi: 10.1038/nm.3390 – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: CR15 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – ident: CR37 – ident: CR53 – volume: 164 start-page: 92 year: 2017 end-page: 102 ident: CR57 article-title: Hough-cnn: deep learning for segmentation of deep brain regions in MRI and ultrasound publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2017.04.002 – volume: 9 start-page: 297 year: 2005 end-page: 314 ident: CR6 article-title: Automated segmentation of the optic nerve head for diagnosis of glaucoma publication-title: Med. Image Anal. doi: 10.1016/j.media.2004.12.004 – ident: CR30 – year: 2006 ident: CR24 publication-title: Statistical Methods for Research Workers – volume: 274 start-page: 146 year: 2016 end-page: 153 ident: CR68 article-title: Fast and robust segmentation of the striatum using deep convolutional neural networks publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2016.10.007 – volume: 7 start-page: 5301 year: 2017 ident: CR8 article-title: Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric mr publication-title: Sci. Rep. doi: 10.1038/s41598-017-05728-9 – ident: CR33 – year: 2019 ident: CR20 publication-title: Reproducibility and Replicability in Science – volume: 6 start-page: 113 year: 2018 end-page: 117 ident: CR70 article-title: Multi-scale structured cnn with label consistency for brain MR image segmentation publication-title: Comput. Methods Biomech. Biomed. Eng. Imaging Vis. doi: 10.1080/21681163.2016.1182072 – volume: 98 start-page: 430 year: 1998 end-page: 438 ident: CR74 article-title: A randomized anova procedure for comparing performance curves publication-title: ICML – volume: 108 start-page: 214 year: 2015 end-page: 224 ident: CR65 article-title: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.12.061 – ident: CR40 – volume: 3 start-page: 35 year: 2009 end-page: 45 ident: CR5 article-title: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2008.2011119 – volume: 533 start-page: 452 year: 2016 ident: CR21 article-title: 1,500 scientists lift the lid on reproducibility publication-title: Nat. News doi: 10.1038/533452a – ident: CR69 – volume: 29 start-page: 102 year: 2019 end-page: 127 ident: CR46 article-title: An overview of deep learning in medical imaging focusing on MRI publication-title: Zeitschrift für Medizinische Physik doi: 10.1016/j.zemedi.2018.11.002 – ident: CR44 – volume: 7 start-page: 2049 year: 2017 ident: CR4 article-title: Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease publication-title: Sci. Rep. doi: 10.1038/s41598-017-01779-0 – ident: CR73 – ident: CR38 – ident: CR17 – ident: CR31 – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: CR14 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 27 start-page: 495 year: 2008 end-page: 508 ident: CR10 article-title: Brain anatomical structure segmentation by hybrid discriminative/generative models publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2007.908121 – ident: CR34 – volume: 35 start-page: 1240 year: 2016 end-page: 1251 ident: CR48 article-title: Brain tumor segmentation using convolutional neural networks in MRI images publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2538465 – ident: CR59 – volume: 129 start-page: 460 year: 2016 end-page: 469 ident: CR72 article-title: Deep MRI brain extraction: a 3D convolutional neural network for skull stripping publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.01.024 – volume: 7 start-page: 5110 year: 2017 ident: CR7 article-title: Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities publication-title: Sci. Rep. doi: 10.1038/s41598-017-05300-5 – volume: 15 start-page: 29 year: 2015 ident: CR19 article-title: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool publication-title: BMC Med. Imaging doi: 10.1186/s12880-015-0068-x – volume: 86 start-page: 420 year: 1979 ident: CR23 article-title: Intraclass correlations: uses in assessing rater reliability publication-title: Psychol. Bull. doi: 10.1037/0033-2909.86.2.420 – ident: CR28 – ident: CR41 – ident: CR62 – volume: 35 start-page: 3 year: 2010 ident: CR2 article-title: Automated medical image segmentation techniques publication-title: J. Med. Phys. Assoc. Med. Phys. India – ident: 69920_CR62 – volume: 40 start-page: 071905 year: 2013 ident: 69920_CR11 publication-title: Med. Phys. doi: 10.1118/1.4810971 – ident: 69920_CR34 doi: 10.25080/Majora-8b375195-003 – volume: 35 start-page: 1229 year: 2016 ident: 69920_CR52 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528821 – ident: 69920_CR61 doi: 10.1145/2647868.2654889 – ident: 69920_CR43 – volume: 35 start-page: 1856 year: 2016 ident: 69920_CR58 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2535222 – volume: 129 start-page: 460 year: 2016 ident: 69920_CR72 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.01.024 – ident: 69920_CR75 – volume: 15 start-page: 29 year: 2015 ident: 69920_CR19 publication-title: BMC Med. Imaging doi: 10.1186/s12880-015-0068-x – ident: 69920_CR28 doi: 10.1007/978-3-642-35289-8_3 – ident: 69920_CR38 doi: 10.1109/ICDMAI.2017.8073516 – volume: 86 start-page: 420 year: 1979 ident: 69920_CR23 publication-title: Psychol. Bull. doi: 10.1037/0033-2909.86.2.420 – volume: 108 start-page: 214 year: 2015 ident: 69920_CR65 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.12.061 – ident: 69920_CR16 doi: 10.1109/CVPR.2015.7298965 – ident: 69920_CR49 doi: 10.1109/ISBI.2016.7493261 – volume: 533 start-page: 452 year: 2016 ident: 69920_CR21 publication-title: Nat. News doi: 10.1038/533452a – ident: 69920_CR73 doi: 10.1155/2016/8356294 – ident: 69920_CR18 – ident: 69920_CR33 – volume: 34 start-page: 1993 year: 2014 ident: 69920_CR54 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – volume: 98 start-page: 430 year: 1998 ident: 69920_CR74 publication-title: ICML – volume: 9 start-page: 297 year: 2005 ident: 69920_CR6 publication-title: Med. Image Anal. doi: 10.1016/j.media.2004.12.004 – ident: 69920_CR32 doi: 10.1007/978-3-030-23987-9_10 – volume-title: Reproducibility and Replicability in Science year: 2019 ident: 69920_CR20 – volume: 7 start-page: 2049 year: 2017 ident: 69920_CR4 publication-title: Sci. Rep. doi: 10.1038/s41598-017-01779-0 – volume: 6 start-page: 113 year: 2018 ident: 69920_CR70 publication-title: Comput. Methods Biomech. Biomed. Eng. Imaging Vis. doi: 10.1080/21681163.2016.1182072 – volume: 30 start-page: 75 year: 2006 ident: 69920_CR26 publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2005.12.001 – ident: 69920_CR51 doi: 10.1007/978-3-319-46976-8_7 – volume: 521 start-page: 436 year: 2015 ident: 69920_CR14 publication-title: Nature doi: 10.1038/nature14539 – ident: 69920_CR37 doi: 10.1109/TPAMI.2021.3059968 – volume: 33 start-page: 341 year: 2002 ident: 69920_CR9 publication-title: Neuron doi: 10.1016/S0896-6273(02)00569-X – volume-title: Deep Learning with Keras year: 2017 ident: 69920_CR63 – volume: 6 start-page: 60 year: 2019 ident: 69920_CR27 publication-title: J. Big Data doi: 10.1186/s40537-019-0197-0 – volume: 274 start-page: 146 year: 2016 ident: 69920_CR68 publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2016.10.007 – volume: 2 start-page: 2 year: 2019 ident: 69920_CR22 publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0079-z – volume: 27 start-page: 495 year: 2008 ident: 69920_CR10 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2007.908121 – ident: 69920_CR53 – ident: 69920_CR60 doi: 10.1145/2733373.2807412 – ident: 69920_CR66 doi: 10.1109/ISBI.2016.7493515 – volume-title: Deep Learning year: 2016 ident: 69920_CR13 – volume: 36 start-page: 61 year: 2017 ident: 69920_CR47 publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – ident: 69920_CR39 – volume: 35 start-page: 1240 year: 2016 ident: 69920_CR48 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2538465 – ident: 69920_CR44 doi: 10.1007/978-3-319-10470-6_39 – volume: 29 start-page: 102 year: 2019 ident: 69920_CR46 publication-title: Zeitschrift für Medizinische Physik doi: 10.1016/j.zemedi.2018.11.002 – ident: 69920_CR29 – volume: 7 start-page: 5110 year: 2017 ident: 69920_CR7 publication-title: Sci. Rep. doi: 10.1038/s41598-017-05300-5 – volume: 3 start-page: 35 year: 2009 ident: 69920_CR5 publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2008.2011119 – ident: 69920_CR41 – ident: 69920_CR17 doi: 10.1007/978-3-319-24574-4_28 – volume-title: Statistical Methods for Research Workers year: 2006 ident: 69920_CR24 – volume: 42 start-page: 60 year: 2017 ident: 69920_CR15 publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – ident: 69920_CR77 – volume: 164 start-page: 92 year: 2017 ident: 69920_CR57 publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2017.04.002 – ident: 69920_CR67 doi: 10.1109/CVPRW.2015.7301312 – ident: 69920_CR35 – volume: 7 start-page: 5301 year: 2017 ident: 69920_CR8 publication-title: Sci. Rep. doi: 10.1038/s41598-017-05728-9 – ident: 69920_CR31 – ident: 69920_CR59 – volume: 35 start-page: 18 year: 2017 ident: 69920_CR55 publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.05.004 – volume: 35 start-page: 3 year: 2010 ident: 69920_CR2 publication-title: J. Med. Phys. Assoc. Med. Phys. India – volume: 35 start-page: 1252 year: 2016 ident: 69920_CR56 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2548501 – volume: 52 start-page: 65 year: 2019 ident: 69920_CR42 publication-title: ACM Comput. Surv. (CSUR) – ident: 69920_CR69 – ident: 69920_CR71 doi: 10.1007/978-3-319-46723-8_54 – ident: 69920_CR64 doi: 10.5281/zenodo.27878 – volume: 44 start-page: 108 year: 2000 ident: 69920_CR25 publication-title: J. Math. Psychol. doi: 10.1006/jmps.1999.1279 – volume: 3 start-page: 100004 year: 2019 ident: 69920_CR45 publication-title: Array doi: 10.1016/j.array.2019.100004 – volume: 10 start-page: 125 year: 2008 ident: 69920_CR1 publication-title: Int. J. Bioelectromagn. – ident: 69920_CR40 – ident: 69920_CR50 doi: 10.1007/978-3-319-46976-8_15 – volume: 6 start-page: 20280 year: 2016 ident: 69920_CR12 publication-title: Sci. Rep. doi: 10.1038/srep20280 – volume: 19 start-page: 1667 year: 2013 ident: 69920_CR3 publication-title: Nat. Med. doi: 10.1038/nm.3390 – ident: 69920_CR36 doi: 10.1109/CVPR.2018.00907 – volume: 7 start-page: 1 year: 2006 ident: 69920_CR76 publication-title: J. Mach. Learn. Res. – ident: 69920_CR30 |
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| Title | Variability and reproducibility in deep learning for medical image segmentation |
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