CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 129-138
DOI: 10.1055/s-0040-1702009
Section 4: Sensor, Signal and Imaging Informatics
Survey
Georg Thieme Verlag KG Stuttgart

Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

Anirudh Choudhary*
1  Department of Computational Science and Engineering, Georgia Institute of Technology, GA, USA
,
Li Tong*
2  Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, GA, USA
,
Yuanda Zhu
3  School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA
,
May D. Wang
1  Department of Computational Science and Engineering, Georgia Institute of Technology, GA, USA
2  Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, GA, USA
3  School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)

Summary

Introduction: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to multiple different datasets.

Objective: In this survey, we review the state-of-the-art DL-based DA methods for medical imaging. We aim to summarize recent advances, highlighting the motivation, challenges, and opportunities, and to discuss promising directions for future work in DA for medical imaging.

Methods: We surveyed peer-reviewed publications from leading biomedical journals and conferences between 2017-2020, that reported the use of DA in medical imaging applications, grouping them by methodology, image modality, and learning scenarios.

Results: We mainly focused on pathology and radiology as application areas. Among various DA approaches, we discussed domain transformation (DT) and latent feature-space transformation (LFST). We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development.

Conclusion: DA has emerged as a promising solution to deal with the lack of annotated training data. Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks. Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.

* Equal Contributing First Authors.


 
  • References

  • 1 Mendelson DS, Rubin DL. Imaging informatics: essential tools for the delivery of imaging services. Acad Radiol 2013; 20 (10) 1195-212
  • 2 Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
  • 3 Adler-Milstein J, Jha AK. Sharing clinical data electronically: a critical challenge for fixing the health care system. JAMA 2012; 307 (16) 1695-6
  • 4 Sharma P, Shamout FE, Clifton DA. Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality. arXiv preprint arXiv:1912.00354; 2019 Dec 1
  • 5 Zhang Y, Wei Y, Zhao P, Niu S, Wu Q, Tan M, Huang J. Collaborative unsupervised domain adaptation for medical image diagnosis. arXiv preprint arXiv:1911.07293; 2019 Nov 17
  • 6 Yao L, Prosky J, Covington B, Lyman K. A strong baseline for domain adaptation and generalization in medical imaging. arXiv preprint arXiv:1904.01638; 2019 Apr 2
  • 7 Pan SJ, Yan Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2009; 22 (10) 1345-59
  • 8 Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Med Image Anal 2020; 101693
  • 9 Dai C, Mo Y, Angelini E, Guo Y, Bai W. Transfer Learning from Partial Annotations for Whole Brain Segmentation. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data; 2019 Oct 13. p. 199-206
  • 10 Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps; 2018. p. 323-50
  • 11 Chartsias A, Joyce T, Dharmakumar R, Tsaftaris SA. Adversarial image synthesis for unpaired multi-modal cardiac data. In: International workshop on simulation and synthesis in medical imaging; 2017 Sep 10. p. 3-13
  • 12 Huo Y, Xu Z, Bao S, Assad A, Abramson RG, Landman BA. Adversarial synthesis learning enables segmentation without target modality ground truth. In: IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018; 2018. p. 1217-20
  • 13 Chen C, Dou Q, Chen H, Qin J, Heng PA. Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence 2019; 33: 865-72
  • 14 Lee B, Newberg A. Neuroimaging in Traumatic Brain Imaging. NeuroRx 2005; Apr 2 (02) 372-83
  • 15 Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: A review. IEEE Rev Biomed Eng 2009 Oct 2009; Oct 30 (02) 147-71
  • 16 Brieu N, Meier A, Kapil A, Schoenmeyer R, Gavriel CG, Caie PD. et al. Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection. arXiv preprint arXiv:1907.04681; 2019 Jul 10
  • 17 Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol 2015; 19 (1A): A68-77
  • 18 Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C. et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv preprint arXiv: 1901.07031; 2019 Jan 21
  • 19 Ciompi F, Geessink O, Bejnordi BE, De Souza GS, Baidoshvili A, Litjens G. et al. The importance of stain normalization in colorectal tissue classification with convolutional networks. In: IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017; 2017. p. 160-3
  • 20 Kushibar K, Valverde S, González-Villà S, Bernal J, Cabezas M, Oliver A. et al. Supervised domain adaptation for automatic sub-cortical brain structure segmentation with minimal user interaction. Sci Rep 2019; 9 (01) 6742
  • 21 Pooch EHP, Ballester PL, Barros RC. Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification. arXiv preprint arXiv: 1909.01940; 2019 Sep 3
  • 22 Lampert T, Merveille O, Schmitz J, Forestier G, Feuerhake F, Wemmert C. Strategies for training stain invariant CNNs. In: IEEE 16th International Symposium on Biomedical Imaging, ISBI 2019; p. 905-9
  • 23 AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys 2018; Mar 45 (03) 1150-8
  • 24 Stacke K, Eilertsen G, Unger J, Lundström C. A Closer Look at Domain Shift for Deep Learning in Histopathology. arXiv preprint arXiv: 1909.11575; 2019 Sep 25
  • 25 Yang X, Dou H, Li R, Wang X, Bian C, Li S. et al. Generalizing Deep Models for Ultrasound Image Segmentation. In: Medical Image Computing and Computer Assisted Intervention, MICCAI 2018; p. 497-505
  • 26 Degel MA, Navab N, Albarqouni S. Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound. ArXiv180500357 Cs. 2018; 11073: 630-7
  • 27 Li H, Loehr T, Wiestler B, Zhang J, Menze B. e-UDA: Efficient Unsupervised Domain Adaptation for Cross-Site Medical Image Segmentation. arXiv preprint arXiv: 2001.09313; 2020 Jan 25
  • 28 Chen C, Dou Q, Chen H, Heng PA. Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest X-ray segmentation. In: International Workshop on Machine Learning in Medical Imaging 2018. p. 143-51
  • 29 Bentaieb A, Hamarneh G. Adversarial stain transfer for histopathology image analysis. IEEE Trans Med Imaging 2018; Mar 37 (03) 792-802
  • 30 Pan Y, Liu M, Lian C, Zhou T, Xia Y, Shen D. Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2018. p. 455-63
  • 31 Ehteshami Bejnordi B, Litjens G, Timofeeva N, Otte-Holler I, Homeyer A, Karssemeijer N. et al. Stain specific standardization of whole-slide histopathological images. IEEE Trans Med Imaging 2016; Feb; 35 (02) 404-15
  • 32 Csurka G. Domain adaptation for visual applications: A comprehensive survey. arXiv preprint arXiv:1702.05374; 2017 Feb 17
  • 33 Wilson G, Cook DJ. A Survey of Unsupervised Deep Domain Adaptation. arXiv preprint arXiv:1812.02849; 2018 Dec 6
  • 34 Wang M, Deng W. Deep visual domain adaptation: A survey. Neurocomputing 2018; 312: 135-53
  • 35 Hoffman J, Tzeng E, Park T, Zhu JY, Isola P, Saenko K. et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation. In: International Conference on Machine Learning 2018. p. 1989-98
  • 36 Dou Q, Ouyang C, Chen C, Chen H, Glocker B, Zhuang X. et al. Pnp-adanet: Plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation. arXiv preprint arXiv:1812.07907; 2018 Dec 19
  • 37 Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017. p. 1125-34
  • 38 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A. et al. Generative adversarial nets. In: Advances in neural information processing systems 2014. p. 2672-80
  • 39 Mahmood F, Chen R, Durr NJ. Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans Med Imaging 2018; Dec; 37 (12) 2572-81
  • 40 Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D. Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017. p. 3722-31
  • 41 Zhang Z, Yang L, Zheng Y. Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern Recognition 2018. p. 9242-9251
  • 42 de Bel T, Hermsen M, Kers J, Van Der Laak J, Litjens G. Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. In: Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning; Proceedings of Machine Learning Research 2019 May 24. p. 151-63
  • 43 Mirza M, Osindero S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784; 2014 Nov 6
  • 44 Yoo D, Kim N, Park S, Paek AS, Kweon IS. Pixel-level domain transfer. In: European Conference on Computer Vision 2016. p. 517-32
  • 45 Liu MY, Tuzel O. Coupled generative adversarial networks. In Advances in neural information processing systems 2016. p. 469-77
  • 46 Madani A, Moradi M, Karargyris A, Syeda-Mahmood T. Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation. In: IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. p. 1038-42
  • 47 Zhao C, Carass A, Lee J, He Y, Prince JL. Whole brain segmentation and labeling from CT using synthetic MR images. In: International Workshop on Machine Learning in Medical Imaging 2017. p. 291-8
  • 48 Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision 2017. p. 2223-32
  • 49 Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2018. p. 8789-97
  • 50 Huang X, Liu MY, Belongie S, Kautz J. Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision, ECCV 2018. p. 172-89
  • 51 Zhang Y, Miao S, Mansi T, Liao R. Task driven generative modeling for unsupervised domain adaptation: Application to X-ray image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2018. p. 599-607
  • 52 Cai J, Zhang Z, Cui L, Zheng Y, Yang L. Towards cross-modal organ translation and segmentation: a cycle-and shape-consistent generative adversarial network. Med Image Anal 2019; 52: 174-84
  • 53 Hiasa Y, Otake Y, Takao M, Matsuoka T, Takashima K, Carass A. et al. Cross-modality image synthesis from unpaired data using CycleGAN. In: International workshop on simulation and synthesis in medical imaging 2018. p. 31-41
  • 54 Jiang J, Hu YC, Tyagi N, Zhang P, Rimner A, Mageras GS. et al. Tumor-aware, adversarial domain adaptation from ct to mri for lung cancer segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2018; pp. 777-785
  • 55 Wang X, Li L, Ye W, Long M, Wang J. Transferable attention for domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence 2019; 33: 5345-52
  • 56 Liu X, Wei X, Yu A, Pan Z. Unpaired Data based Cross-domain Synthesis and Segmentation Using Attention Neural Network. In: Asian Conference on Machine Learning 2019. p. 987-1000
  • 57 Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K. et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999; 2018 Apr 11
  • 58 Nguyen H, Luo S, Ramos F. Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2020. p. 409-21
  • 59 Chen C, Dou Q, Chen H, Qin J, Heng PA. Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans Med Imaging 2020; 39 (07) 2494-505
  • 60 Rozantsev A, Salzmann M, Fua P. Beyond sharing weights for deep domain adaptation. IEEE transactions Trans Pattern Anal Mach Intell 2019; 41 (04) 801-14
  • 61 Sun B, Saenko K. Deep coral: Correlation alignment for deep domain adaptation. In: European conference on computer vision 2016. p. 443-50
  • 62 Bhushan Damodaran B, Kellenberger B, Flamary R, Tuia D, Courty N. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV) 2018. p. 447-63
  • 63 Sun B, Feng J, Saenko K. Return of frustratingly easy domain adaptation. In: Thirtieth AAAI Conference on Artificial Intelligence 2016 Mar 2
  • 64 Kang G, Jiang L, Yang Y, Hauptmann AG. Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019. p. 4893-902
  • 65 Tzeng E, Hoffman J, Saenko K, Darrell T. Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017. p. 7167-76
  • 66 Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F. et al. Domain-adversarial training of neural networks. The Journal of Machine Learning Research 2016; 17 (01) 2096-30
  • 67 Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474. 2014 Dec 10
  • 68 Tsai YH, Hung WC, Schulter S, Sohn K, Yang MH, Chandraker M. Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018. p. 7472-81
  • 69 Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D. Domain separation networks. In: Advances in neural information processing systems 2016. p. 343-51
  • 70 Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W. Deep reconstruction-classification networks for unsupervised domain adaptation. In: European Conference on Computer Vision 2016. p. 597-613
  • 71 Zhu X, Thung KH, Adeli E, Zhang Y, Shen D. Maximum mean discrepancy based multiple kernel learning for incomplete multimodality neuroimaging data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2017. p. 72-80
  • 72 Venkataramani R, Ravishankar H, Anamandra S. Towards Continuous Domain Adaptation For Medical Imaging. In: IEEE 16th International Symposium on Biomedical Imaging, ISBI 2019. p. 443-6
  • 73 Zhuang J, Chen Z, Zhang J, Zhang D, Cai Z. Domain adaptation for retinal vessel segmentation using asymmetrical maximum classifier discrepancy. In: Proceedings of the ACM Turing Celebration Conference-China 2019. p. 1-6
  • 74 Bermúdez-Chacón R, Altingövde O, Becker C, Salzmann M, Fua P. Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images. IEEE Trans Med Imaging 2020; 39 (04) 1256-67
  • 75 Zhang Y, Chen H, Wei Y, Zhao P, Cao J, Fan X. et al. From whole slide imaging to microscopy: Deep microscopy adaptation network for histopathology cancer image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 360-8
  • 76 Lafarge MW, Pluim JP, Eppenhof KA, Moeskops P, Veta M. Domain-adversarial neural networks to address the appearance variability of histopathology images. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 2017. p. 83-91
  • 77 Kamnitsas K, Baumgartner C, Ledig C, Newcombe V, Simpson J, Kane A. et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: International conference on information processing in medical imaging 2017. p. 597-609
  • 78 Orbes-Arteaga M, Varsavsky T, Sudre CH, Eaton-Rosen Z, Haddow LJ, Sørensen L. , et al. Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data 2019. p. 54-62
  • 79 Shanis Z, Gerber S, Gao M, Enquobahrie A. Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data 2019. p. 28-36
  • 80 Oliveira H, Ferreira E, Dos Santos JA. Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation. IEEE Access 2020 May 1
  • 81 Ouyang C, Kamnitsas K, Biffi C, Duan J, Rueckert D. Data efficient unsupervised domain adaptation for Cross-Modality image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 669-77
  • 82 Tellez D, Litjens G, Bándi P, Bulten W, Bokhorst JM, Ciompi F, van der Laak J. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal 2019 Dec 2019; Dec 1 58: 101544
  • 83 Peng X, Bai Q, Xia X, Huang Z, Saenko K, Wang B. Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision 2019. p. 1406-15
  • 84 Dong J, Cong Y, Sun G, Zhong B, Xu X. What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation. arXiv preprint arXiv:2004.11500; 2020 Apr 24
  • 85 Saito K, Watanabe K, Ushiku Y, Harada T. Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018. p. 3723-32
  • 86 Cao Z, Ma L, Long M, Wang J. Partial adversarial domain adaptation. In: Proceedings of the European Conference on Computer Vision, ECCV 2018. p. 135-50
  • 87 Panareda Busto P, Gall J. Open set domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision 2017. p. 754-63
  • 88 Goldberger J, Ben-Reuven E. Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations 2017
  • 89 Valindria VV, Lavdas I, Bai W, Kamnitsas K, Aboagye EO, Rockall AG. et al. Domain adaptation for MRI organ segmentation using reverse classification accuracy. arXiv preprint arXiv:1806.00363; 2018 Jun 1
  • 90 Arora S, Ge R, Liang Y, Ma T, Zhang Y. Generalization and equilibrium in generative adversarial nets (gans). In: Proceedings of the 34th International Conference on Machine Learning Volume 70 2017 Aug 6. p. 224-32
  • 91 Martin A, Lon B. Towards principled methods for training generative adversarial networks. In: NIPS 2016 Workshop on Adversarial Training
  • 92 Chu C, Zhmoginov A, Sandler M. Cyclegan, a master of steganography. arXiv preprint arXiv:1712.02950; 2017 Dec 8
  • 93 Shrivastava A, Adorno W, Ehsan L, Ali SA, Moore SR, Amadi BC. et al. Self-Attentive Adversarial Stain Normalization. arXiv preprint arXiv:1909.01963; 2019 Sep 4
  • 94 Wang Y, Zhong Z, Hua J. DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D/4D Lung Models from Single-View Projections by Deep Deformation Network. IEEE Trans Vis Comput Graph 2019; 26 (01) 960-70
  • 95 Jac Jr CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS. et al. Update on hypothetical model of Alzheimer’s disease biomarkers. Lancet Neurology 2013; 12 (02) 207
  • 96 Mahmood F, Durr NJ. Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal 2018 Aug 2018; Aug 1 48: 230-43
  • 97 Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R. Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017. p. 2107-16
  • 98 Ghorbani A, Natarajan V, Coz D, Liu Y. DermGAN: Synthetic Generation of Clinical Skin Images with Pathology. arXiv preprint arXiv:1911.0871
  • 99 Gholami A, Subramanian S, Shenoy V, Himthani N, Yue X, Zhao S. et al. A novel domain adaptation framework for medical image segmentation. In: International MICCAI Brainlesion Workshop 2018. p. 289-98
  • 100 Tang Y, Tang Y, Sandfort V, Xiao J, Summers RM. TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 431-40
  • 101 Kapil A, Wiestler T, Lanzmich S, Silva A, Steele K, Rebelatto M. et al. DASGAN--Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images. arXiv preprint arXiv:1906.11118; 2019 Jun 26
  • 102 Yang J, Dvornek NC, Zhang F, Chapiro J, Lin M, Duncan JS. Unsupervised domain adaptation via disentangled representations: Application to cross-modality liver segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 255-63
  • 103 Dou Q, Ouyang C, Chen C, Chen H, Glocker B, Zhuang X. et al. Pnp-adanet: Plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation. arXiv preprint arXiv:1812.07907; 2018 Dec 19
  • 104 Dong N, Kampffmeyer M, Liang X, Wang Z, Dai W, Xing E. Unsupervised domain adaptation for automatic estimation of cardiothoracic ration. In: International Conference on Medical Image Computing and Computer-assisted intervention 2018. p. 544-52
  • 105 Javanmardi M, Tasdizen T. Domain adaptation for biomedical image segmentation using adversarial training. In: IEEE 15th International Symposium on Biomedical Imaging 2018. p. 554-8
  • 106 Novosad P, Fonov V, Collins DL. Unsupervised domain adaptation for the automated segmentation of neuroanatomy in MRI: a deep learning approach. bioRxiv preprint bioRxiv:845537 2019 Jan 1
  • 107 Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X. Adversarial domain adaptation for classification of prostate histopathology whole-slide images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2018. p. 201-9
  • 108 Wang S, Yu L, Li K, Yang X, Fu CW, Heng PA. Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 102-10
  • 109 Yan W, Wang Y, Xia M, Tao Q. Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation. IEEE Signal Processing Letters 2019; 26 (11) 1593-7
  • 110 Hou X, Liu J, Xu B, Liu B, Chen X, Ilyas M. et al. Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019. p. 101-9
  • 111 Zhao H, Li H, Maurer-Stroh S, Gui Y, Deng Q, Cheng L. Supervised segmentation of un-annotated retinal fundus images by synthesis. IEEE Trans Med Imaging 2018; 38 (01) 46-56
  • 112 Tong L, Wu H, Wang MD. CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images. J Am Med Inform Assoc 2019; Nov; 26 (11) 1286-96