Kelly BS, Decide C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. Radiology synthetic intelligence: a scientific evaluate and analysis of strategies (RAISE). Eur Radiol. 2022;32(11):7998–8007.
Bahadir CD, Omar M, Rosenthal J, Marchionni L, Liechty B, Pisapia DJ, et al. Synthetic intelligence purposes in histopathology. Nat Rev Electr Eng. 2024. https://doi.org/10.1038/s44287-023-00012-7.
Thomsen Okay, Iversen L, Titlestad TL, Winther O. Systematic evaluate of machine studying for analysis and prognosis in dermatology. J Dermatol Deal with. 2020;31(5):496–510.
Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Synthetic intelligence and machine studying in nuclear medication: future views. In: Seminars in nuclear medication. vol. 51. Elsevier; 2021. pp. 170–7.
Du W, Rao N, Liu D, Jiang H, Luo C, Li Z, et al. Evaluation on the Functions of Deep Studying within the Evaluation of Gastrointestinal Endoscopy Photographs. IEEE Entry. 2019;7:142053–69. https://doi.org/10.1109/ACCESS.2019.2944676.
Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. Chexpert: A big chest radiograph dataset with uncertainty labels and knowledgeable comparability. In: Proceedings of the AAAI convention on synthetic intelligence. vol. 33. 2019. pp. 590–7.
Li J, Zhu G, Hua C, Feng M, Bennamoun B, Li P, et al. A scientific assortment of medical picture datasets for deep studying. ACM Comput Surv. 2023;56(5):1–51.
Varoquaux G, Cheplygina V. Machine studying for medical imaging: methodological failures and proposals for the long run. NPJ Digit Med. 2022;5(1):48.
Lundervold AS, Lundervold A. An outline of deep studying in medical imaging specializing in MRI. Z Med Phys. 2019;29(2):102–27.
Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Getting ready medical imaging information for machine studying. Radiology. 2020;295(1):4–15.
Xu J. A evaluate of self-supervised studying strategies within the area of medical picture evaluation. Int J Picture Graph Sign Course of (IJIGSP). 2021;13(4):33–46.
Shurrab S, Duwairi R. Self-supervised studying strategies and purposes in medical imaging evaluation: a survey. PeerJ Laptop Science. 2022;8:e1045.
Huang SC, Pareek A, Jensen M, Lungren MP, Yeung S, Chaudhari AS. Self-supervised studying for medical picture classification: a scientific evaluate and implementation pointers. NPJ Digit Med. 2023;6(1):74.
Deng J, Dong W, Socher R, Li LJ, Li Okay, Fei-Fei L. Imagenet: A big-scale hierarchical picture database. In: 2019 IEEE convention on laptop imaginative and prescient and sample recognition. New York: IEEE; 2009. p. 248–55.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139–44.
Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Fashions. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors. Advances in Neural Info Processing Methods, vol. 33. New York: Curran Associates, Inc.; 2020. p. 6840–51.
Vincent P, Larochelle H, Bengio Y, Manzagol P-A. Extracting and composing strong options with denoising autoencoders. In: Proceedings of the twenty fifth Worldwide Convention on Machine Studying. ICML ’08. New York: Affiliation for Computing Equipment; 2008. p. 1096–1103.
Zhang R, Isola P, Efros AA. Colourful picture colorization. In: Laptop Imaginative and prescient–ECCV 2016: 14th European Convention, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Half III 14. Berlin: Springer; 2016. p. 649–66.
Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA. Context encoders: Characteristic studying by inpainting. In: 2016 IEEE Convention on Laptop Imaginative and prescient and Sample Recognition (CVPR). Los Alamitos: IEEE Laptop Society; 2016. p. 2536–2544.
Doersch C, Gupta A, Efros AA. Unsupervised visible illustration studying by context prediction. In: 2015 IEEE Worldwide Convention on Laptop Imaginative and prescient (ICCV). Los Alamitos: IEEE Laptop Society; 2015. p. 1422–1430.
Gidaris S, Singh P, Komodakis N. Unsupervised illustration studying by predicting picture rotations. In: Worldwide Convention on Studying Representations. New York: Curran Associates, Inc.; 2018.
Noroozi M, Favaro P. Unsupervised studying of visible representations by fixing jigsaw puzzles. In: Laptop Imaginative and prescient–ECCV 2016: 14th European Convention, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Half VI. Berlin: Springer; 2016. p. 69–84.
Chen T, Kornblith S, Norouzi M, Hinton G. A easy framework for contrastive studying of visible representations. In: Worldwide convention on machine studying. Cambridge: PMLR; 2020. p. 1597–607.
He Okay, Fan H, Wu Y, Xie S, Girshick R. Momentum distinction for unsupervised visible illustration studying. In: 2020 IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (CVPR). Los Alamitos: IEEE Laptop Society; 2020. p. 9726–9735.
Mishra AK, Roy P, Bandyopadhyay S, Das SK. CR-SSL: A intently associated self-supervised studying primarily based method for bettering breast ultrasound tumor segmentation. Int J Imaging Syst Technol. 2022;32(4):1209–20. Writer: John Wiley and Sons Inc.
Zbontar J, Jing L, Misra I, LeCun Y, Deny S. Barlow twins: Self-supervised studying by way of redundancy discount. In: Worldwide Convention on Machine Studying. Cambridge: PMLR; 2021. p. 12310–20.
Grill JB, Strub F, Altché F, Tallec C, Richemond P, Buchatskaya E, et al. Bootstrap your personal latent-a new method to self-supervised studying. Adv Neural Inf Course of Syst. 2020;33:21271–84.
Bardes A, Ponce J, LeCun Y. VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Studying. In: Worldwide Convention on Studying Representations. 2022. https://openreview.internet/discussion board?id=xm6YD62D1Ub. Accessed 11 Dec 2022.
Lee JD, Lei Q, Saunshi N, Zhuo J. Predicting what you already know helps: Provable self-supervised studying. Adv Neural Inf Course of Syst. 2021;34:309–23.
HaoChen JZ, Wei C, Gaidon A, Ma T. Provable ensures for self-supervised deep studying with spectral contrastive loss. Adv Neural Inf Course of Syst. 2021;34:5000–11.
Balestriero R, LeCun Y. Contrastive and non-contrastive self-supervised studying get better world and native spectral embedding strategies. In: Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho Okay, Oh A, editors. Advances in Neural Info Processing Methods, vol. 35. New York: Curran Associates, Inc.; 2022. p. 26671–26685.
Zhou HY, Yu S, Bian C, Hu Y, Ma Okay, Zheng Y. Evaluating to be taught: Surpassing imagenet pretraining on radiographs by evaluating picture representations. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12261 LNCS:398–407. ISBN: 9783030597092 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of frequent thorax ailments. In: Proceedings of the IEEE convention on laptop imaginative and prescient and sample recognition. 2017. pp. 2097–106.
Shih G, Wu CC, Halabi SS, Kohli MD, Prevedello LM, Cook dinner TS, et al. Augmenting the nationwide institutes of well being chest radiograph dataset with knowledgeable annotations of potential pneumonia. Radiol Artif Intell. 2019;1(1):e180041.
Dong N, Voiculescu I. Federated Contrastive Studying for Decentralized Unlabeled Medical Photographs. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12903 LNCS:378–87. ISBN: 9783030871987 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Liao W, Xiong H, Wang Q, Mo Y, Li X, Liu Y, et al. MUSCLE: Multi-task Self-supervised Continuous Studying to Pre-train Deep Fashions for X-Ray Photographs of A number of Physique Components. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13438 LNCS:151–61. ISBN: 9783031164514 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Dalla Serra F, Jacenków G, Deligianni F, Dalton J, O’Neil AQ. Enhancing Picture Representations by way of MoCo Pre-training for Multimodal CXR Classification. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13413 LNCS:623–35. ISBN: 9783031120527 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Ridzuan M, Bawazir A, Gollini Navarrete I, Almakky I, Yaqub M. Self-supervision and Multi-task Studying: Challenges in Superb-Grained COVID-19 Multi-class Classification from Chest X-rays. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13413 LNCS:234–50. ISBN: 9783031120527 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Ma DA, Hosseinzadeh Taher MR, Pang J, Islam NU, Haghighi F, Gotway MB, et al. Benchmarking and Boosting Transformers for Medical Picture Classification. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13542 LNCS:12–22. ISBN: 9783031168512 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Liu F, Tian Y, Cordeiro FR, Belagiannis V, Reid I, Carneiro G. Self-supervised Imply Instructor for Semi-supervised Chest X-Ray Classification. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12966 LNCS:426–36. ISBN: 9783030875886 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Haghighi F, Taher M, Gotway MB, Liang J. Dira: Discriminative, restorative, and adversarial studying for self-supervised medical picture evaluation. In: 2022 IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (CVPR). Los Alamitos: IEEE Laptop Society; 2022. p. 20792–20802.
Pang J, Haghighi F, Ma DA, Islam NU, Hosseinzadeh Taher MR, Gotway MB, et al. POPAR: Patch Order Prediction and Look Restoration for Self-supervised Medical Picture Evaluation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13542 LNCS:77–87. ISBN: 9783031168512 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Azizi S, Mustafa B, Ryan F, Beaver Z, Freyberg J, Deaton J, Loh A, Karthikesalingam A, Kornblith S, Chen T, Natarajan V, Norouzi M. Large self-supervised fashions advance medical picture classification. In: 2021 IEEE/CVF Worldwide Convention on Laptop Imaginative and prescient (ICCV). Los Alamitos: IEEE Laptop Society; 2021. p. 3458–3468.
Zhao X, Zhou S. Quick mixing of onerous unfavorable samples for contrastive studying and use for covid-19. In: Proceedings of the 4th Worldwide Convention on Large Knowledge Applied sciences. ICBDT ’21. New York: Affiliation for Computing Equipment; 2022. p. 6–12.
Feki I, Ammar S, Kessentini Y. Self-supervised Studying for COVID-19 Detection from Chest X-ray Photographs. Commun Comput Inf Sci. 2022;1589 CCIS:78–89. ISBN: 9783031082764 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Bencevic M, Habijan M, Galic I, Pizurica A. Self-supervised Studying as a Means to Scale back the Want for Labeled Knowledge in Medical Picture Evaluation. In: European Sign Processing Convention. vol. 2022-August. European Sign Processing Convention, EUSIPCO. New York: IEEE; 2022. p. 1328–32. ISSN: 22195491.
Han Y, Chen C, Tewfik A, Ding Y, Peng Y. Pneumonia Detection On Chest X-Ray Utilizing Radiomic Options And Contrastive Studying. In: 2021 IEEE 18th Worldwide Symposium on Biomedical Imaging (ISBI). New York: IEEE; 2021. p. 247–51. ISSN: 1945-8452.
Gazda M, Plavka J, Gazda J, Drotar P. Self-Supervised Deep Convolutional Neural Community for Chest X-Ray Classification. IEEE Entry. 2021;9:151972–82. Writer: Institute of Electrical and Electronics Engineers Inc.
Konwer A, Prasanna P. Medical final result prediction in COVID-19 utilizing self-supervised imaginative and prescient transformer representations. In: Medical Imaging 2022: Laptop-Aided Analysis. vol. 12033. Bellingham: SPIE; 2022. p. 33–7.
Hao Y, Wang Y, Wang X. Self-supervised Pretraining for Covid-19 and Different Pneumonia Detection from Chest X-ray Photographs. Lect Notes Knowledge Eng Commun Technol. 2022;89:1000–7. Writer: Springer Science and Enterprise Media Deutschland GmbH.
Li G, Togo R, Ogawa T, Haseyama M. Self-Information Distillation primarily based Self-Supervised Studying for Covid-19 Detection from Chest X-Ray Photographs. In: ICASSP 2022 – 2022 IEEE Worldwide Convention on Acoustics, Speech and Sign Processing (ICASSP). 2022. pp. 1371–5. ISSN: 2379-190X.
Wei Z, Park S, Kim J. A Triplet Distinction Studying of World and Native Representations for Unannotated Medical Photographs. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13564 LNCS:181–90. ISBN: 9783031169182 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Nguyen NQ, Le TS. A Semi-Supervised Studying Methodology to Treatment the Lack of Labeled Knowledge. In: 2021 fifteenth Worldwide Convention on Superior Computing and Functions (ACOMP); 2021. pp. 78–84. ISSN: 2688-0202.
Yang J, Shi R, Wei D, Liu Z, Zhao L, Ke B, et al. MedMNIST v2-A big-scale light-weight benchmark for 2D and 3D biomedical picture classification. Sci Knowledge. 2023;10(1):41.
Mondal AK. COVID-19 prognosis utilizing restricted chest X-ray pictures. Utilized Tender Computing. 2022;122:108867.
Pavlova M, Terhljan N, Chung AG, Zhao A, Surana S, Aboutalebi H, et al. Covid-net cxr-2: An enhanced deep convolutional neural community design for detection of covid-19 instances from chest x-ray pictures. Entrance Med. 2022;9:861680.
Chen X, He Okay. Exploring easy siamese illustration studying. In: Proceedings of the IEEE/CVF convention on laptop imaginative and prescient and sample recognition. 2021. pp. 15750–8.
Zhou HY, Lu C, Yang S, Han X, Yu Y. Preservational Studying Improves Self-supervised Medical Picture Fashions by Reconstructing Numerous Contexts. In: Proceedings of the IEEE Worldwide Convention on Laptop Imaginative and prescient. New York: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 3479–89. ISSN: 15505499.
Park J, Kwak IY, Lim C. A deep studying mannequin with self-supervised studying and a spotlight mechanism for covid-19 analysis utilizing chest x-ray pictures. Electronics (Switzerland). 2021;10(16):1996.
Tang X, Zhou C, Chen L, Wen Y. Enhancing Medical Picture Classification by way of Augmentation-based Pre-training. In: Proceedings – 2021 IEEE Worldwide Convention on Bioinformatics and Biomedicine, BIBM 2021. New York: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 1538–41.
Abbas A, Abdelsamea MM, Gaber MM. 4S-DT: Self-Supervised Tremendous Pattern Decomposition for Switch Studying with Utility to COVID-19 Detection. IEEE Trans Neural Netw Study Syst. 2021;32(7):2798–808. Writer: Institute of Electrical and Electronics Engineers Inc.
Ji Z, Shaikh MA, Moukheiber D, Srihari SN, Peng Y, Gao M. Enhancing Joint Studying of Chest X-Ray and Radiology Report by Phrase Area Alignment. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12966 LNCS:110–9. ISBN: 9783030875886 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Müller P, Kaissis G, Zou C, Rueckert D. Radiological Studies Enhance Pre-training for Localized Imaging Duties on Chest X-Rays. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13435 LNCS:647–57. ISBN: 9783031164422 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Tiu E, Talius E, Patel P, Langlotz CP, Ng AY, Rajpurkar P. Professional-level detection of pathologies from unannotated chest x-ray pictures by way of self-supervised studying. Nature Biomedical Engineering. 2022;6(12):1399–406.
Johnson AE, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng C-y, Mark RG, Horng S. MIMIC-CXR, a de-identified publicly accessible database of chest radiographs with free-text reviews. Scientific Knowledge. 2019;6(1):317.
Truong Vu YN, Tsue T, Su J, Singh S. An improved mammography malignancy mannequin with self-supervised studying. In: Mazurowski M A DK, editor. Progress in Biomedical Optics and Imaging – Proceedings of SPIE, vol. 11597. Bellingham: SPIE; 2021. ISSN: 16057422.
You Okay, Lee S, Jo Okay, Park E, Kooi T, Nam H. Intra-class Contrastive Studying Improves Laptop Aided Analysis of Breast Most cancers in Mammography. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13433 LNCS:55–64. ISBN: 9783031164361 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Cao Z, Yang Z, Tang Y, Zhang Y, Han M, Xiao J, et al. Supervised Contrastive Pre-training forMammographic Triage Screening Fashions. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12907 LNCS:129–39. ISBN: 9783030872335 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Saidnassim N, Abdikenov B, Kelesbekov R, Akhtar MT, Jamwal P. Self-supervised Visible Transformers for Breast Most cancers Analysis. In: 2021 Asia-Pacific Sign and Info Processing Affiliation Annual Summit and Convention, APSIPA ASC 2021 – Proceedings. New York: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 423–7.
Taleb A, Rohrer C, Bergner B, De Leon G, Rodrigues JA, Schwendicke F, et al. Self-Supervised Studying Strategies for Label-Environment friendly Dental Caries Classification. Diagnostics. 2022;12(5):1237.
Armato SG III, McLennan G, Bidaut L, McNitt-Grey MF, Meyer CR, Reeves AP, et al. The lung picture database consortium (LIDC) and picture database useful resource initiative (IDRI): a accomplished reference database of lung nodules on CT scans. Med Phys. 2011;38(2):915–31.
Setio AAA, Traverso A, De Bel T, Berens MS, Van Den Bogaard C, Cerello P, et al. Validation, comparability, and mixture of algorithms for automated detection of pulmonary nodules in computed tomography pictures: the LUNA16 problem. Med Picture Anal. 2017;42:1–13.
Zhou Z, Sodha V, Pang J, Gotway MB, Liang J. Fashions Genesis. Medical Picture Evaluation. 2021;67:101840.
Haghighi F, Hosseinzadeh Taher MR, Zhou Z, Gotway MB, Liang J. Studying semantics-enriched illustration by way of self-discovery, self-classification, and self-restoration. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12261 LNCS:137–47. ISBN: 9783030597092 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Feng R, Zhou Z, Gotway MB, Liang J. Parts2Whole: Self-supervised Contrastive Studying by way of Reconstruction. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12444 LNCS:85–95. ISBN: 9783030605476 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zhai P, Cong H, Zhu E, Zhao G, Yu Y, Li J. MVCNet: Multiview contrastive community for unsupervised illustration studying for 3-D CT lesions. In: IEEE Transactions on Neural Networks and Studying Methods, 1–15. 2022. https://doi.org/10.1109/TNNLS.2022.3203412.
Huang H, Wu R, Li Y, Peng C. Self-Supervised Switch Studying Primarily based on Area Adaptation for Benign-Malignant Lung Nodule Classification on Thoracic CT. IEEE J Biomed Well being Inf. 2022;26(8):3860–71. Writer: Institute of Electrical and Electronics Engineers Inc.
Niu C, Wang G. Unsupervised contrastive studying primarily based transformer for lung nodule detection. Phys Med Biol. 2022;67(20):204001.
Gai L, Chen W, Gao R, Chen YW, Qiao X. Utilizing Imaginative and prescient Transformers in 3-D Medical Picture Classifications. In: 2022 IEEE Worldwide Convention on Picture Processing (ICIP). New York: 2022. p. 696–700. ISSN: 2381-8549.
Guo Z, Islam NU, Gotway MB, Liang J. Discriminative, Restorative, and Adversarial Studying: Stepwise Incremental Pretraining. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13542 LNCS:66–76. ISBN: 9783031168512 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Tajbakhsh N, Gotway MB, Liang J. Laptop-aided pulmonary embolism detection utilizing a novel vessel-aligned multi-planar picture illustration and convolutional neural networks. In: Medical Picture Computing and Laptop-Assisted Intervention–MICCAI 2015: 18th Worldwide Convention, Munich, Germany, October 5-9, 2015, Proceedings, Half II 18. New York: Springer; 2015. p. 62–9.
Islam NU, Gehlot S, Zhou Z, Gotway MB, Liang J. Looking for an Optimum Method for Laptop-Aided Pulmonary Embolism Detection. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12966 LNCS:692–702. ISBN: 9783030875886 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Xie Z, Zhang Z, Cao Y, Lin Y, Bao J, Yao Z, Dai Q, Hu H. Simmim: a easy framework for masked picture modeling. In: 2022 IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (CVPR). Los Alamitos: IEEE Laptop Society; 2022. p. 9643–9653.
Zhuang X, Li Y, Hu Y, Ma Okay, Yang Y, Zheng Y. Self-supervised function studying for 3d medical pictures by enjoying a rubik’s dice. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2019;11767 LNCS:420–8. ISBN: 9783030322502 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zhu J, Li Y, Hu Y, Ma Okay, Zhou SK, Zheng Y. Rubik’s Dice+: A self-supervised function studying framework for 3D medical picture evaluation. Med Picture Anal. 2020;64:101746.
Zhu J, Li Y, Ding L, Zhou SK. Aggregative Self-supervised Characteristic Studying from Restricted Medical Photographs. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13438 LNCS:57–66. ISBN: 9783031164514 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Li Y, Wei D, Chen J, Cao S, Zhou H, Zhu Y, et al. Environment friendly and Efficient Coaching of COVID-19 Classification Networks with Self-Supervised Twin-Monitor Studying to Rank. IEEE J Biomed Well being Inf. 2020;24(10):2787–97. Writer: Institute of Electrical and Electronics Engineers Inc.
Ewen N, Khan N. Focused self supervision for classification on a small covid-19 ct scan dataset. In: Proceedings – Worldwide Symposium on Biomedical Imaging, vol. 2021-April. New York: IEEE; 2021. p. 1481–5. ISSN: 19457928.
Yang X, He X, Zhao J, Zhang Y, Zhang S, Xie P. COVID-CT-dataset: a CT scan dataset about COVID-19. 2020. arXiv preprint arXiv:200313865. Printed on-line 2020.
Lu H, Dai Q. A self-supervised COVID-19 CT recognition system with a number of regularizations. Comput Biol Medi. 2022;150:106149.
Cohen Hochberg D, Greenspan H, Giryes R. A self supervised StyleGAN for picture annotation and classification with extraordinarily restricted labels. IEEE Transactions on Medical Imaging. 2022;41(12):3509–19.
Caron M, Touvron H, Misra I, Jegou H, Mairal J, Bojanowski P, Joulin A. Rising properties in self-supervised imaginative and prescient transformers. In: 2021 IEEE/CVF Worldwide Convention on Laptop Imaginative and prescient (ICCV). Los Alamitos: IEEE Laptop Society; 2021. p. 9630–9640.
Rahimzadeh M, Attar A, Sakhaei SM. A completely automated deep learning-based community for detecting COVID-19 from a brand new and enormous lung CT scan dataset. Biomed Sign Course of Management. 2021;68:102588.
Gao Y, Wang H, Liu X, Huang N, Wang G, Zhang S. A Denoising Self-supervised Method for COVID-19 Pneumonia Lesion Segmentation with Restricted Annotated CT Photographs. In: Proceedings of the Annual Worldwide Convention of the IEEE Engineering in Drugs and Biology Society, EMBS. New York: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 3705–8. ISSN: 1557170X.
Roth H, Farag A, Turkbey EB, Lu L, Liu J, Summers RM. Knowledge From Pancreas-CT. Most cancers Imaging Arch. 2016. https://wiki.cancerimagingarchive.internet/x/eIlXAQ. Accessed 3 Mar 2023.
Zheng H, Qian L, Qin Y, Gu Y, Yang J. Enhancing the slice interplay of two.5D CNN for automated pancreas segmentation. Medical Physics. 2020;47(11):5543–54. Writer: John Wiley and Sons Ltd.
Tao X, Li Y, Zhou W, Ma Okay, Zheng Y. Revisiting Rubik’s Dice: Self-supervised Studying with Quantity-Smart Transformation for 3D Medical Picture Segmentation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12264 LNCS:238–48. ISBN: 9783030597184 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Yang Z, Xie L, Zhou W, Huo X, Wei L, Lu J, Tian Q, Tang S. VoxSeP: semi-positive voxels help self-supervised 3d medical segmentation. Multimedia Methods. 2023;29(1):33–48.
Taleb A, Loetzsch W, Danz N, Severin J, Gaertner T, Bergner B, Lippert C. 3d self-supervised strategies for medical imaging. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors. Advances in Neural Info Processing Methods, vol. 33. New York: Curran Associates, Inc.; 2020. p. 18158–18172.
Zhang X, Feng S, Zhou Y, Zhang Y, Wang Y. SAR: Scale-Conscious Restoration Studying for 3D Tumor Segmentation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12902 LNCS:124–33. ISBN: 9783030871956 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani Okay, Van Ginneken B, et al. A big annotated medical picture dataset for the event and analysis of segmentation algorithms. 2019. arXiv preprint arXiv:190209063. Printed on-line 2019.
Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, et al. The liver tumor segmentation benchmark (lits). Med Picture Anal. 2023;84:102680.
Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A. MICCAI multi-atlas labeling past the cranial vault-workshop and problem (2015). 2015. https://doi.org/10.7303/syn3193805.
Tang Y, Yang D, Li W, Roth HR, Landman B, Xu D, Nath V, Hatamizadeh A. Self-supervised pre-training of swin transformers for 3d medical picture evaluation. In: 2022 IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (CVPR). Los Alamitos: IEEE Laptop Society; 2022. p. 20698–20708.
Jiang J, Tyagi N, Tringale Okay, Crane C, Veeraraghavan H. Self-supervised 3D Anatomy Segmentation Utilizing Self-distilled Masked Picture Transformer (SMIT). Lecture Notes in Laptop Science (together with subseries Lecture Notes in Synthetic Intelligence and Lecture Notes in Bioinformatics). 2022;13434 LNCS:556–66. ISBN: 9783031164392 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zheng H, Han J, Wang H, Yang L, Zhao Z, Wang C, et al. Hierarchical Self-supervised Studying for Medical Picture Segmentation Primarily based on Multi-domain Knowledge Aggregation. Lect Notes Comput Sci (Includ Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12901 LNCS:622–32. ISBN: 9783030871925 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Liu C, Zhang X, Si W, Ni X. Multiview self-supervised segmentation for OARs delineation in radiotherapy. Proof-Primarily based Complementary and Different Drugs. 2021;2021:1–5.
Yu C, Hu J, Li G, Zhu S, Bai S, Yi Z. Segmentation for areas of curiosity in radiotherapy by self-supervised studying. Knowl-Primarily based Syst. 2022;256:109370.
Francis S, Pooloth G, Singam SBS, Puzhakkal N, Pulinthanathu Narayanan P, Pottekkattuvalappil Balakrishnan J. SABOS-Web: Self-supervised consideration primarily based community for automated organ segmentation of head and neck CT pictures. Worldwide Journal of Imaging Methods and Expertise. 2023;33(1):175–91.
Heller N, Sathianathen N, Kalapara A, Walczak E, Moore Okay, Kaluzniak H, et al. The kits19 problem information: 300 kidney tumor instances with medical context, ct semantic segmentations, and surgical outcomes. 2020. arXiv preprint arXiv:190400445. Printed on-line 2020.
Zhao Z, Yang G. Unsupervised Contrastive Studying of Radiomics and Deep Options for Label-Environment friendly Tumor Classification. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12902 LNCS:252–61. ISBN: 9783030871956 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Dong H, Iwamoto Y, Han X, Lin L, Hu H, Cai X, et al. Case Discrimination: Self-supervised Characteristic Studying for the Classification of Focal Liver Lesions. Sensible Innov Syst Technol. 2021;242:241–9. ISBN: 9789811630125 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Ma C, Liu Z, Track Y, Qiu C, Yu A, Zhang J. Self-supervision Adversarial Studying Community for Liver Lesion Classification. Lecture Notes in Laptop Science (together with subseries Lecture Notes in Synthetic Intelligence and Lecture Notes in Bioinformatics). 2021;12736 LNCS:540–52. ISBN: 9783030786083 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Xu L, Yang C, Zhang F, Cheng X, Wei Y, Fan S, et al. Deep Studying Utilizing CT Photographs to Grade Clear Cell Renal Cell Carcinoma: Growth and Validation of a Prediction Mannequin. Cancers. 2022;14(11):2574.
Jana A, Qu H, Minacapelli CD, Catalano C, Rustgi V, Metaxas D. Liver Fibrosis And NAS Scoring From CT Photographs Utilizing Self-Supervised Studying And Texture Encoding. In: 2021 IEEE 18th Worldwide Symposium on Biomedical Imaging (ISBI). New York: 2021. p. 1553–7. ISSN: 1945-8452.
Shou Y, Meng T, Ai W, Xie C, Liu H, Wang Y. Object Detection in Medical Photographs Primarily based on Hierarchical Transformer and Masks Mechanism. Comput Intell Neurosci. 2022;2022:5863782.
Chen L, Bentley P, Mori Okay, Misawa Okay, Fujiwara M, Rueckert D. Self-supervised studying for medical picture evaluation utilizing picture context restoration. Medical Picture Evaluation. 2019;58:101539.
Kraft M, Pieczyński D, Siemionow KK. Overcoming Knowledge Shortage for Coronary Vessel Segmentation By means of Self-supervised Pre-training. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;13110 LNCS:369–78. ISBN: 9783030922375 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zhuang X, Bai W, Track J, Zhan S, Qian X, Shi W, et al. Multiatlas entire coronary heart segmentation of CT information utilizing conditional entropy for atlas rating and choice. Med Phys. 2015;42(7):3822–33.
Dong N, Kampffmeyer M, Voiculescu I. Self-supervised Multi-task Illustration Studying for Sequential Medical Photographs. Lecture Notes in Laptop Science (together with subseries Lecture Notes in Synthetic Intelligence and Lecture Notes in Bioinformatics). 2021;12977 LNAI:779–94. ISBN: 9783030865221 Writer: Springer Science and Enterprise Media Deutschland GmbH.
McSweeney DM, Henderson EG, van Herk M, Weaver J, Bromiley PA, Inexperienced A, et al. Switch studying for data-efficient belly muscle segmentation with convolutional neural networks. Med Phys. 2022;49(5):3107–20. Writer: John Wiley and Sons Ltd.
Xue B, Liu Z, Wang Q, Tang Q, Huang J, Zhou Y. SNU-Web: a self-supervised deep studying technique for pneumothorax segmentation on chest CT. In: 2022 IEEE Worldwide Symposium on Circuits and Methods (ISCAS). 2022. pp. 60–4. ISSN: 2158-1525.
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani Okay, Kirby J, et al. The multimodal mind tumor picture segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2014;34(10):1993–2024.
Kayal S, Chen S, de Bruijne M. Area-of-interest guided supervoxel inpainting for self-supervision. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12261 LNCS:500–9. ISBN: 9783030597092 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Taleb A, Lippert C, Klein T, Nabi M. Multimodal Self-supervised Studying for Medical Picture Evaluation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12729 LNCS:661–73. ISBN: 9783030781903 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Huang J, Li H, Li G, Wan X. Attentive Symmetric Autoencoder for Mind MRI Segmentation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13435 LNCS:203–13. ISBN: 9783031164422 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, et al. The Alzheimer’s illness neuroimaging initiative. Neuroimaging Clin. 2005;15(4):869–77.
LaMontagne PJ, Benzinger TL, Morris JC, Keefe S, Hornbeck R, Xiong C, Grant E, Hassenstab J, Moulder Okay, Vlassenko AG, Raichle ME, Cruchaga C, Marcus D. OASIS-3: Longitudinal neuroimaging, medical, and cognitive dataset for regular ageing and alzheimer illness. medRxiv. 2019. Printed on-line 2019.
Mahmood U, Rahman MM, Fedorov A, Lewis N, Fu Z, Calhoun VD, et al. Complete MILC: Generalizing Discovered Dynamics Throughout Duties, Datasets, and Populations. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12267 LNCS:407–17. ISBN: 9783030597276 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Fedorov A, Wu L, Sylvain T, Luck M, Deramus TP, Bleklov D, et al. On self-supervised multimodal illustration studying: An software to alzheimer’s illness. In: Proceedings – Worldwide Symposium on Biomedical Imaging. vol. 2021-April. New York: IEEE; 2021. p. 1548–52. ISSN: 19457928.
Fedorov A, Sylvain T, Geenjaar E, Luck M, Wu L, DeRamus TP, et al. Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer’s Illness. In: 2021 IEEE ninth Worldwide Convention on Healthcare Informatics (ICHI). New York: 2021. p. 23–30. ISSN: 2575-2634.
Zhao Q, Liu Z, Adeli E, Pohl KM. Longitudinal self-supervised studying. Med Picture Anal. 2021;71:102051.
Ouyang J, Zhao Q, Adeli E, Zaharchuk G, Pohl KM. Self-supervised studying of neighborhood embedding for longitudinal MRI. Med Picture Anal. 2022;82:102571.
Dufumier B, Gori P, Victor J, Grigis A, Wessa M, Brambilla P, et al. Contrastive Studying with Steady Proxy Meta-data for 3D MRI Classification. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12902 LNCS:58–68. ISBN: 9783030871956 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Cao G, Wang Y, Zhang M, Zhang J, Kang G, Xu X. Multiview Lengthy-Quick Spatial Contrastive Studying For 3D Medical Picture Evaluation. In: ICASSP 2022 – 2022 IEEE Worldwide Convention on Acoustics, Speech and Sign Processing (ICASSP). New York: 2022. p. 1226–30. ISSN: 2379-190X.
Seyfioğlu MS, Liu Z, Kamath P, Gangolli S, Wang S, Grabowski T, et al. Mind-Conscious Replacements for Supervised Contrastive Studying in Detection of Alzheimer’s Illness. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13431 LNCS:461–70. ISBN: 9783031164309 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Osin J, Wolf L, Gurevitch G, Keynan JN, Fruchtman-Steinbok T, Or-Borichev A, et al. Studying Private Representations from fMRI by Predicting Neurofeedback Efficiency. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12267 LNCS:469–78. ISBN: 9783030597276 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Lu Q, Li Y, Ye C. White Matter Tract Segmentation with Self-supervised Studying. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12267 LNCS:270–9. ISBN: 9783030597276 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil Okay, et al. The WU-Minn human connectome undertaking: an outline. Neuroimage. 2013;80:62–79.
Lu Q, Li Y, Ye C. Volumetric white matter tract segmentation with nested self-supervised studying utilizing sequential pretext duties. Med Picture Anal. 2021;72:102094.
Kuijf HJ, Biesbroek JM, De Bresser J, Heinen R, Andermatt S, Bento M, et al. Standardized evaluation of automated segmentation of white matter hyperintensities and outcomes of the WMH segmentation problem. IEEE Trans Med Imaging. 2019;38(11):2556–68.
Chang F, Wu C, Wang Y, Zhang Y, Chen X, Tian Q. Boundary-Enhanced Self-supervised Studying for Mind Construction Segmentation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13431 LNCS:14–23. ISBN: 9783031164309 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zoetmulder R, Gavves E, Caan M, Marquering H. Area- and task-specific switch studying for medical segmentation duties. Comput Strategies Prog Biomed. 2022;214:106539.
Tran M, Ly L, Hua BS, Le N. SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Much less Labeled Knowledge. In: Proceedings – Worldwide Symposium on Biomedical Imaging, vol. 2022-March. New York: IEEE; 2022. ISSN: 19457928.
Chaitanya Okay, Erdil E, Karani N, Konukoglu E. Contrastive studying of worldwide and native options for medical picture segmentation with restricted annotations. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors. Advances in Neural Info Processing Methods, vol. 33. New York: Curran Associates, Inc.; 2020. p. 12546–12558.
Armato SG III, Huisman H, Drukker Okay, Hadjiiski L, Kirby JS, Petrick N, et al. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance pictures. J Med Imaging. 2018;5(4):044501.
Fernandez-Quilez A, Eftestol T, Kjosavik SR, Goodwin M, Oppedal Okay. Contrasting Axial T2W MRI for Prostate Most cancers Triage: A Self-Supervised Studying Method. In: Proceedings – Worldwide Symposium on Biomedical Imaging. vol. 2022-March. New York: IEEE; 2022. ISSN: 19457928.
Wang Y, Track D, Wang W, Rao S, Wang X, Wang M. Self-supervised studying and semi-supervised studying for multi-sequence medical picture classification. Neurocomputing. 2022;513:383–94. Writer: Elsevier B.V.
Bolous A, Seetharaman A, Bhattacharya I, Fan RE, Soerensen SJC, Chen L, et al. Clinically important prostate most cancers detection on MRI with self-supervised studying utilizing picture context restoration. In: Mazurowski M A DK, editor. Progress in Biomedical Optics and Imaging – Proceedings of SPIE, vol. 11597. Bellingham: SPIE; 2021. ISSN: 16057422.
Bai W, Chen C, Tarroni G, Duan J, Guitton F, Petersen SE, et al. Self-Supervised Studying for Cardiac MR Picture Segmentation by Anatomical Place Prediction. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2019;11765 LNCS:541–9. ISBN: 9783030322441 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D. Self-supervision with Superpixels: Coaching Few-Shot Medical Picture Segmentation With out Annotation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2020;12374 LNCS:762–80. ISBN: 9783030585259 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zhuang X. Multivariate combination mannequin for myocardial segmentation combining multi-source pictures. IEEE Trans Sample Anal Mach Intell. 2018;41(12):2933–46.
Wu Y, Zeng D, Wang Z, Shi Y, Hu J. Federated Contrastive Studying for Volumetric Medical Picture Segmentation. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12903 LNCS:367–77. ISBN: 9783030871987 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Kiyasseh D, Swiston A, Chen R, Chen A. Segmentation of Left Atrial MR Photographs by way of Self-supervised Semi-supervised Meta-learning. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12902 LNCS:13–24. ISBN: 9783030871956 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Zhong H, Wu J, Zhao W, Xu X, Hou R, Zhao L, et al. A Self-supervised Studying Primarily based Framework for Computerized Coronary heart Failure Classification on Cine Cardiac Magnetic Resonance Picture. In: Proceedings of the Annual Worldwide Convention of the IEEE Engineering in Drugs and Biology Society, EMBS. New York: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 2887–90. ISSN: 1557170X.
Jamaludin A, Kadir T, Zisserman A. Self-supervised studying for spinal MRIs. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2017;10553 LNCS:294–302. ISBN: 9783319675572 Writer: Springer Verlag.
Pfirrmann CW, Metzdorf A, Zanetti M, Hodler J, Boos N. Magnetic resonance classification of lumbar intervertebral disc degeneration. Backbone. 2001;26(17):1873–8.
Kuang X, Cheung JPY, Ding X, Zhang T. SpineGEM: A Hybrid-Supervised Mannequin Era Technique Enabling Correct Backbone Illness Classification with a Small Coaching Dataset. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12902 LNCS:145–54. ISBN: 9783030871956 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Nguyen XB, Lee GS, Kim SH, Yang HJ. Self-Supervised Studying Primarily based on Spatial Consciousness for Medical Picture Evaluation. IEEE Entry. 2020;8:162973–81.
Atito S, Anwar SM, Awais M, Kittler J. SB-SSL: Slice-Primarily based Self-supervised Transformers for Knee Abnormality Classification from MRI. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13559 LNCS:86–95. ISBN: 9783031167591 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Jiao M, Liu H, Yang Z, Tian S, Ouyang H, Li Y, et al. Self-supervised Studying Primarily based on a Pre-trained Methodology for the Subtype Classification of Spinal Tumors. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13574 LNCS:58–67. ISBN: 9783031172656 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Perek S, Amit M, Hexter E. Self Supervised Contrastive Studying on A number of Breast Modalities Boosts Classification Efficiency. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2021;12928 LNCS:117–27. ISBN: 9783030876012 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Lin Z, Huang R, Ni D, Wu J, Luo B. Masked Video Modeling with Correlation-Conscious Contrastive Studying for Breast Most cancers Analysis in Ultrasound. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13543 LNCS:105–14. ISBN: 9783031168758 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound pictures. Knowledge Transient. 2020;28:104863.
Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, et al. Automated breast ultrasound lesions detection utilizing convolutional neural networks. IEEE J Biomed Well being Inform. 2017;22(4):1218–26.
Anand D, Annangi P, Sudhakar P. Benchmarking Self-Supervised Illustration Studying from 1,000,000 Cardiac Ultrasound pictures. In: Proceedings of the Annual Worldwide Convention of the IEEE Engineering in Drugs and Biology Society, EMBS, vol. 2022-July. New York: IEEE; 2022. p. 529–32. ISSN: 1557170X.
Chen X, Fan H, Girshick R, He Okay. Improved baselines with momentum contrastive studying. 2020. arXiv preprint arXiv:200304297. Printed on-line 2020.
Saeed M, Muhtaseb R, Yaqub M. Contrastive Pretraining for Echocardiography Segmentation with Restricted Knowledge. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinforma). 2022;13413 LNCS:680–91. ISBN: 9783031120527 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, et al. Video-based AI for beat-to-beat evaluation of cardiac operate. Nature. 2020;580(7802):252–6.
Leclerc S, Smistad E, Pedrosa J, Østvik A, Cervenansky F, Espinosa F, et al. Deep studying for segmentation utilizing an open large-scale dataset in 2D echocardiography. IEEE Trans Med Imaging. 2019;38(9):2198–210.
Dezaki FT, Luong C, Ginsberg T, Rohling R, Gin Okay, Abolmaesumi P, et al. Echo-SyncNet: Self-Supervised Cardiac View Synchronization in Echocardiography. IEEE Trans Med Imaging. 2021;40(8):2092–104.
Shusharina N, Heinrich MP, Huang R. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Knowledge: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings vol. 12587. Springer Nature; 2021. p. xiii-iv.
Xiang Z, Zhuo Q, Zhao C, Deng X, Zhu T, Wang T, et al. Self-supervised multi-modal fusion community for multi-modal thyroid ultrasound picture analysis. Comput Biol Med. 2022;150. Writer: Elsevier Ltd.
Guo Y, He Y, Li S, Shu T, Gao L. World Correlation and Native Geometric Info Coupled Channel Distinction Studying for Thyroid Nodule Threat Stratification. In: 2021 IEEE Worldwide Convention on Bioinformatics and Biomedicine (BIBM). New York: 2021. p. 868–75.
Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, et al. ACR thyroid imaging, reporting and information system (TI-RADS): white paper of the ACR TI-RADS committee. J Am Coll Radiol. 2017;14(5):587–95.
Jiao J, Droste R, Drukker L, Papageorghiou AT, Noble JA. Self-Supervised Illustration Studying for Ultrasound Video. In: Proceedings – Worldwide Symposium on Biomedical Imaging. vol. 2020-April. New York: IEEE; 2020. p. 1847–50. ISSN: 19457928.
Qi H, Collins S, Noble JA. Information-guided pretext studying for utero-placental interface detection. Lecture Notes in Laptop Science (together with subseries Lecture Notes in Synthetic Intelligence and Lecture Notes in Bioinformatics). 2020;12261 LNCS:582–93. ISBN: 9783030597092 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Liu C, Qiao M, Jiang F, Guo Y, Jin Z, Wang Y. TN-USMA Web: Triple normalization-based gastrointestinal stromal tumors classification on multicenter EUS pictures with ultrasound-specific pretraining and meta consideration. Med Phys. 2021;48(11):7199–214. Writer: John Wiley and Sons Ltd.
Zhou Z, Zhao C, Qiao H, Wang M, Guo Y, Wang Q, et al. RATING: Medical knowledge-guided rheumatoid arthritis evaluation from multimodal ultrasound pictures by way of deep studying. Patterns. 2022;3(10):100592.
Basu S, Singla S, Gupta M, Rana P, Gupta P, Arora C. Unsupervised Contrastive Studying of Picture Representations from Ultrasound Movies with Arduous Unfavorable Mining. Lecture Notes in Laptop Science (together with subseries Lecture Notes in Synthetic Intelligence and Lecture Notes in Bioinformatics). 2022;13434 LNCS:423–33. ISBN: 9783031164392 Writer: Springer Science and Enterprise Media Deutschland GmbH.
Born J, Brändle G, Cossio M, Disdier M, Goulet J, Roulin J, et al. POCOVID-Web: automated detection of COVID-19 from a brand new lung ultrasound imaging dataset (POCUS). 2021. arXiv preprint arXiv:200412084. Printed on-line 2021.
Zheng R, Zhong Y, Yan S, Solar H, Shen H, Huang Okay. MsVRL Self-supervised multiscale visible illustration studying by way of cross-level consistency for medical picture segmentation. IEEE Transactions on Medical Imaging. 2023;42(1):91–102.
Stein A, Wu C, Carr C, Shih G, Dulkowski J, Kalpathy-Cramer J, et al. RSNA Pneumonia Detection Problem. Mountain View: Kaggle; 2018.
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, et al. Figuring out medical diagnoses and treatable ailments by image-based deep studying. Cell. 2018;172(5):1122–31.
Antonelli M, Reinke A, Bakas S, Farahani Okay, Kopp-Schneider A, Landman BA, et al. The medical segmentation decathlon. Nat Commun. 2022;13(1):4128.
Colak E, Kitamura FC, Hobbs SB, Wu CC, Lungren MP, Prevedello LM, et al. The RSNA pulmonary embolism CT dataset. Radiol Artif Intell. 2021;3(2):e200254.
Mei X, Liu Z, Robson PM, Marinelli B, Huang M, Doshi A, et al. RadImageNet: an open radiologic deep studying analysis dataset for efficient switch studying. Radiol Artif Intell. 2022;4(5):e210315.
Cheplygina V, de Bruijne M, Pluim JP. Not-so-supervised: a survey of semi-supervised, multi-instance, and switch studying in medical picture evaluation. Med Picture Anal. 2019;54:280–96.
Solatidehkordi Z, Zualkernan I. Survey on latest developments in medical picture classification utilizing semi-supervised studying. Appl Sci. 2022;12(23):12094.
Jiao R, Zhang Y, Ding L, Xue B, Zhang J, Cai R, Jin C. Studying with restricted annotations: A survey on deep semi-supervised studying for medical picture segmentation. Computer systems in Biology and Drugs. 2024;169:107840.
