Semi-supervised deep learning for ocular image classification

ilustraciones, gráficas, tablas

Autores:: Arrieta Ramos, José Miguel

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Semi-supervised deep learning for ocular image classification
dc.title.translated.spa.fl_str_mv	Aprendizaje profundo semi-supervisado para la clasificación de imágenes oculares
title	Semi-supervised deep learning for ocular image classification
spellingShingle	Semi-supervised deep learning for ocular image classification 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
title_short	Semi-supervised deep learning for ocular image classification
title_full	Semi-supervised deep learning for ocular image classification
title_fullStr	Semi-supervised deep learning for ocular image classification
title_full_unstemmed	Semi-supervised deep learning for ocular image classification
title_sort	Semi-supervised deep learning for ocular image classification
dc.creator.fl_str_mv	Arrieta Ramos, José Miguel
dc.contributor.advisor.spa.fl_str_mv	González Osorio, Fabio Augusto Perdomo Charry, Oscar Julián
dc.contributor.author.spa.fl_str_mv	Arrieta Ramos, José Miguel
dc.contributor.referee.spa.fl_str_mv	Romero Castro, Edgar Eduardo Toledo Cortés, Santiago
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
dc.subject.decs.eng.fl_str_mv	Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning
dc.subject.decs.spa.fl_str_mv	Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático
dc.subject.proposal.eng.fl_str_mv	Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning
dc.subject.proposal.spa.fl_str_mv	Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
description	ilustraciones, gráficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-21T19:42:01Z
dc.date.available.none.fl_str_mv	2022-06-21T19:42:01Z
dc.date.issued.none.fl_str_mv	2022-06-03
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81620
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81620 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Albarqouni, Shadi ; Baur, Christoph ; Achilles, Felix ; Belagiannis, Vasileios ; Demirci, Stefanie ; Navab, Nassir: AggNet: Deep Learning From Crowds for Mito- sis Detection in Breast Cancer Histology Images. En: IEEE Transactions on Medical Imaging 35 (2016), Nr. 5, p. 1313–1321. – ISSN 1558254X America, Central ; Rico, Puerto ; Republic, Dominican: Diabetes in South Central America - 2021 Diabetes in South Central America - 2021. (2021) Asiri, Norah ; Hussain, Muhammad ; Al, Fadwa ; Alzaidi, Nazih: Deep learning ba- sed computer-aided diagnosis systems for diabetic retinopathy : A survey. En: Artificial Intelligence In Medicine 99 (2019), Nr. December 2018, p. 101701. – ISSN 0933–3657 Azizi, Shekoofeh ; Mustafa, Basil ; Ryan, Fiona ; Beaver, Zachary ; Freyberg, Jan ; Deaton, Jonathan ; Loh, Aaron ; Karthikesalingam, Alan ; Kornblith, Simon ; Chen, Ting ; Natarajan, Vivek ; Norouzi, Mohammad: Big Self-Supervised Models Advance Medical Image Classification. , p. 1–19 B, Kevis-kokitsi M. ; Pont-tuset, Jordi ; Arbel, Pablo: Deep Retinal Image Un- derstanding. 1 (2016), p. 140–148. ISBN 978–3–319–46722–1 Beede, Emma ; Baylor, Elizabeth ; Hersch, Fred ; Iurchenko, Anna ; Wilcox, Lauren ; Raumviboonsuk, Dr. P. ; Vardoulakis, Laura: A Human-Centered Eva- luation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. En: CHI 2020 Paper (2020), p. 1–12. ISBN 9781450367080 Bernardes, Rui: Optical Coherence Tomography. 2015. – ISBN 9783642274091 Berthelot, David ; Oliver, Avital ; Carlini, Nicholas ; Goodfellow, Ian ; Raf- fel, Colin ; Papernot, Nicolas: MixMatch : A Holistic Approach to Semi-Supervised Learning. (2019), Nr. NeurIPS, p. 1–11 Bhaskaranand, Malavika ; Ramachandra, Chaithanya ; Bhat, Sandeep ; Cua- dros, Jorge ; Nittala, Muneeswar G. ; Sadda, Srinivas R. ; Solanki, Kaushal: The value of automated diabetic retinopathy screening with the EyeArt system: A study of more than 100,000 consecutive encounters from people with diabetes. En: Diabetes Technology and Therapeutics 21 (2019), Nr. 11, p. 635–643. – ISSN 15578593 Bock, Rudiger ; Meier, Jorg ; Nyul, Laszlo G. ; Hornegger, Joachim ; Michelson, Georg: Glaucoma risk index: Automated glaucoma detection from color fundus images. En: Medical Image Analysis 14 (2010), Nr. 3, p. 471–481. – ISSN 13618415 Burlina, Philippe M. ; Joshi, Neil ; Pacheco, Katia D. ; Freund, David E. ; Kong, Jun ; Bressler, Neil M.: Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk among Patients with Age-Related Macular Degeneration. En: JAMA Ophthalmology 136 (2018), Nr. 12, p. 1359–1366. – ISSN 21686165 Charry, Oscar Julian P. ; Arevalo, John ; Gonza ́lez, Fabio A.: Combining morpho- metric features and convolutional networks fusion for glaucoma diagnosis, SPIE-Intl Soc Optical Eng, 11 2017. – ISBN 9781510616332, p. 57 Chaves, Levy ; Bissoto, Alceu ; Valle, Eduardo ; Avila, Sandra: An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis. (2021), Nr. Ic, p. 1–12 Chen, Ting ; Kornblith, Simon ; Norouzi, Mohammad ; Hinton, Geo↵rey: A Simple Framework for Contrastive Learning of Visual Representations. (2019) Chen, Ting ; Kornblith, Simon ; Swersky, Kevin ; Norouzi, Mohammad ; Hin- ton, Geo↵rey: Big Self-Supervised Models are Strong Semi-Supervised Learners. (2020), Nr. NeurIPS, p. 1–18 Chen, Xiangyu ; Xu, Yanwu ; Wong, Damon Wing K. ; Wong, Tien Y. ; Liu, Jiang: Glaucoma detection based on deep convolutional neural network, Institute of Electrical and Electronics Engineers Inc., 11 2015. – ISBN 9781424492718, p. 715–718 Christopher, Mark ; Belghith, Akram ; Bowd, Christopher ; Proudfoot, Ja- mes A. ; Goldbaum, Michael H. ; Weinreb, Robert N. ; Girkin, Christopher A. ; Liebmann, Je↵rey M. ; Zangwill, Linda M.: Performance of Deep Learning Architec- tures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. En: Scientific Reports 8 (2018), 12. – ISSN 20452322 Ciga, Ozan ; Xu, Tony ; Martel, Anne L.: Self supervised contrastive learning for digital histopathology. (2020), 11 Cui, Lei ; Feng, Jun ; Yang, Lin: Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification. En: Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (2019), p. 2333–2337. ISBN 9781538654880 De Fauw, Jerey ; Ledsam, Joseph R. ; Romera-Paredes, Bernardino ; Nikolov, Stanislav ; Tomasev, Nenad ; Blackwell, Sam ; Askham, Harry ; Glorot, Xavier ; O‘Donoghue, Brendan ; Visentin, Daniel ; et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. En: Nature Medicine 24 (2018), Nr. 9, p. 1342–1350. – ISBN 4159101801 Deng, J ; Dong, W ; Socher, R ; Li, L.-J. ; Li, K ; Fei-Fei, L: ImageNet: A Large-Scale Hierarchical Image Database. En: CVPR09, 2009 Diaz-Pinto, Andres ; Colomer, Adrian ; Naranjo, Valery ; Morales, Sandra ; Xu, Yanwu ; Frangi, Alejandro F.: Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. En: IEEE Transactions on Medical Imaging 38 (2019), p. 2211–2218 Diaz-Pinto, Andres ; Morales, Sandra ; Naranjo, Valery ; Kohler, Thomas ; Mossi, Jose M. ; Navea, Amparo: CNNs for automatic glaucoma assessment using fundus images: An extensive validation. En: BioMedical Engineering Online 18 (2019), 3. – ISSN 1475925X F.A, Perdomo Charry O. J; Gozalez O.: A Systematic Review of Deep Learning Methods Applied to Ocular Images. 30 (2019), Nr. 1 Farsiu, Sina ; Chiu, Stephanie J. ; O‘Connell, Rachelle V. ; Folgar, Francisco A. ;Yuan, Eric ; Izatt, Joseph A. ; Toth, Cynthia A.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. En: Ophthalmology 121 (2014), Nr. 1, p. 162–172. – ISSN 01616420 Fukushima, Kunihiko: Neocognitron: A hierarchical neural network capable of visual pattern recognition. En: Neural Networks 1 (1988), Nr. 2, p. 119–130. – ISSN 08936080 Gargeya, Rishab ; Leng, Theodore: Automated Identification of Diabetic Retino- pathy Using Deep Learning. En: Ophthalmology 124 (2017), Nr. 7, p. 962–969. – ISSN 15494713 Ghamdi, Manal A. ; Li, Mingqi ; mottaleb Mohamed, Mohamed A. ; Shousha, Abou: Semi-supervised Transfer Learning For Convolutional Neural Networks For Glau- coma Detection. ISBN 9781538646588 Gholami, Peyman: Developing algorithms for the analysis of retinal Optical Coherence Tomography images. (2018) Grigg, Tom G. ; Busbridge, Dan ; Ramapuram, Jason ; Webb, Russ: Do Self- Supervised and Supervised Methods Learn Similar Visual Representations? (2021) Gulshan, Varun ; Peng, Lily ; Coram, Marc ; Stumpe, Martin C. ; Wu, Derek ; Narayanaswamy, Arunachalam ; Venugopalan, Subhashini ; Widner, Kasumi ; Madams, Tom ; Cuadros, Jorge ; Kim, Ramasamy ; Raman, Rajiv ; Nelson,Philip C. ; Mega, Jessica L. ; Webster, Dale R.: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. 94043 (2016), p. 1–9 Hansen, Colin B. ; Nath, Vishwesh ; Gao, Riqiang ; Bermudez, Camilo ; Huo, Yuankai ; Sandler, Kim L. ; Massion, Pierre P. ; Blume, Je↵rey D. ; Lasko, Tho- mas A. ; Landman, Bennett A.: Semi-supervised Machine Learning with MixMatch and Equivalence Classes. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12446 LNCS (2020), p. 112–121. – ISBN 9783030611651 Hervella, Alvaro S. ; Rouco, Jos ́e ; Novo, Jorge ; Ortega, Marcos: Self-supervised multimodal reconstruction of retinal images over paired datasets. En: Expert Systems with Applications 161 (2020). – ISSN 09574174 Hinton, Geoffrey ; Vinyals, Oriol ; Dean, Jeff: Distilling the Knowledge in a Neural Network. (2015), p. 1–9 de Jong, Paulus T.: Age-Related Macular Degeneration. (2006), p. 1474–1485 Kaku, Aakash ; Upadhya, Sahana ; Razavian, Narges: Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning. (2021) Karen Simonyan, Andrew Z.: Very Deep Convolutional Networks For Large-Scale Image Recognition. (2015), p. 1–14 Krause, Jonathan ; Gulshan, Varun ; Rahimy, Ehsan ; Karth, Peter ; Widner, Kasumi ; Corrado, Greg S. ; Peng, Lily ; Webster, Dale R.: Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. En: Ophthalmology 125 (2018), Nr. 8, p. 1264–1272. – ISSN 15494713 Krizhevsky, Alex ; Hinton, Geo↵rey E.: ImageNet Classification with Deep Convo- lutional Neural Networks. , p. 1–9 Lecun, Yann: 1.1 Deep Learning Hardware: Past, Present, and Future. (2019), p. 12–19. ISBN 9781538685310 Lecun, Yann ; Bottou, Leon ; Bengio, Yoshua ; Ha, Patrick: Gradient-Based Lear- ning Applied to Document Recognition. (1998), Nr. November, p. 1–46 Lee, Cecilia S. ; Baughman, Doug M. ; Lee, Aaron Y.: Deep Learning Is E↵ective for Classifying Normal versus Age-Related Macular Degeneration OCT Images. En: Kidney International Reports 1 (2017), Nr. 4, p. 322–327. – ISSN 24680249 Lee, Dong-Hyun: Pseudo-label: The simple and e cient semi-supervised learning method for deep neural networks. En: ICML 2013 Workshop: Challenges in Repre- sentation Learning (2013), p. 1–6 Litjens, Geert ; Kooi, Thijs ; Bejnordi, Babak E. ; Setio, Arnaud Arindra A. ; Ciompi, Francesco ; Ghafoorian, Mohsen ; van der Laak, Jeroen A W M. ; van Ginneken, Bram ; Sa ́nchez, Clara I.: A survey on deep learning in medical image analysis. En: Medical Image Analysis 42 (2017), Nr. December 2012, p. 60–88. – ISSN 13618423 Liu, Jiamin ; Yao, Jianhua ; Bagheri, Mohammadhadi ; Summers, Ronald M.: A Semi-Supervised CNN Learning Method with Pseudo-class Labels for Atherosclerotic Vascular Calcification Detection. En: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (2019), p. 57 Liu, Sijie ; B, Jingmin X. ; Wu, Jiayi ; Shi, Peiwen: Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening. (2019), p. 60–68. ISBN 9783030329563 Mabu, Shingo ; Kido, Shoji ; Hirano, Yasushi ; Kuremoto, Takashi: Unsupervised and semi-supervised learning for e cient opacity annotation of di↵use lung diseases, 2019. – ISBN 9781510627758, p. 8 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, 2018, p. 1038–1042 Marcus, Gary: Deep Learning: A Critical Appraisal. (2018), p. 1–27 Moura, Joaquim D. ; Novo, Jorge ; Ortega, Marcos: Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema. 2019. – ISBN 9781728120096 Organisation, World H.: World report on vision. 2019. 2019. – 1–160 p.. – ISBN 9789241516570 Ota ́lora, Sebastian ; Marini, Niccolo ́ ; Muller, Henning ; Atzori, Manfredo: Semi- weakly Supervised Learning for Prostate Cancer Image Classification with Teacher- Student Deep Convolutional Networks. En: Lecture Notes in Computer Science (inclu- ding subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinforma- tics) 12446 LNCS (2020), p. 193–203. – ISBN 9783030611651 Otalora Sebastian; Perdomo Oscar, Gonzalez Fabio ; Muller H.: Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images. En: MICCAI Workshop on Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS) 10552 (2017), p. 180–189. ISBN 978–3–319– 67533–6 Perdomo, Oscar ; Otalora, Sebastian ; Gonzalez, Fabio A. ; Meriaudeau, Fabri- ce ; Muller, Henning: OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. En: Proceedings - Inter- national Symposium on Biomedical Imaging 2018-April (2018), Nr. Isbi, p. 1423–1426. – ISBN 9781538636367 Perek, Shaked ; Amit, Mika ; Hexter, Efrat: Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance. Vol. 12928 LNCS. Springer International Publishing, 2021. – 117–127 p.. – ISBN 9783030876012 Pooch, Eduardo H. ; Ballester, Pedro ; Barros, Rodrigo C.: Semi-supervised Classification of Chest Radiographs. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12446 LNCS (2020), p. 172–179. – ISBN 9783030611651 Quellec, Gwenol ́e ; Charrie ́re, Katia ; Boudi, Yassine ; Cochener, B ́eatrice ; Lamard, Mathieu: Deep image mining for diabetic retinopathy screening. En: Medical Image Analysis 39 (2017), p. 178–193. – ISSN 13618423 Rakhlin, Alexander: Diabetic Retinopathy detection through integration of Deep Learning classification framework. (2018), p. 1–11 Ren, Kaiming He Xiangyu Zhang S.: Deep Residual Learning for Image Recognition. En: Enzyme and Microbial Technology 19 (2015), Nr. 2, p. 107–117. – ISBN 0141–0229 Scale, International Clinical Diabetic Retinopathy Disease S.: International Clinical Diabetic Retinopathy. (2002), Nr. October, p. 8500 Schmidhuber, Jurgen: Deep Learning in Neural Networks : An Overview. (2014), p. 1 – 88 Sohn, Kihyuk ; Berthelot, David ; Zizhao, Chun-liang L. ; Nicholas, Zhang ; Cu- buk, Ekin D. ; Kurakin, Alex ; Zhang, Han ; Raffel, Colin: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidenc. Sun, Hong ; Saeedi, Pouya ; Karuranga, Suvi ; Pinkepank, Moritz ; Ogurtsova, Katherine ; Duncan, Bruce B. ; Stein, Caroline ; Basit, Abdul ; Chan, Juliana C. ; Mbanya, Jean C. ; Pavkov, Meda E. ; Ramachandaran, Ambady ; Wild, Sarah H. ; James, Steven ; Herman, William H. ; Zhang, Ping ; Bommer, Christian ; Kuo, Shihchen ; Boyko, Edward J. ; Magliano, Dianna J.: IDF Diabetes Atlas: Global, Sun, Wenqing ; Tseng, Tzu Liang B. ; Zhang, Jianying ; Qian, Wei: Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. En: Computerized Medical Imaging and Graphics 57 (2017), p. 4–9. – ISSN 18790771 Suzuki, Kenji: Overview of deep learning in medical imaging. En: Radiological Physics and Technology 10 (2017), Nr. 3, p. 257–273. – ISSN 18650341 Tarvainen, Antti ; Valpola, Harri: Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results. En: Advan- ces in Neural Information Processing Systems 2017-December (2017), p. 1196–1205. – ISSN 10495258 Toledo-cort, Santiago ; Pava, Melissa De L. ; Perd, Oscar: Diabetic Retinopathy Diagnosis and Uncertainty. Van Tulder, Gijs ; De Bruijne, Marleen: Combining Generative and Discrimi- native Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines. En: IEEE Transactions on Medical Imaging 35 (2016), Nr. 5, p. 1262–1272. – ISSN 1558254X Voets, Mike ; Kajsa, Mollersen ; Bongo, Lars: Reproduction study using public data of : Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. (2019), p. 1–11 Vu, Yen Nhi T. ; Wang, Richard ; Balachandar, Niranjan ; Liu, Can ; Ng, An- drew Y. ; Rajpurkar, Pranav: MedAug: Contrastive learning leveraging patient me- tadata improves representations for chest X-ray interpretation. (2021), p. 1–14 Wang, Haohan ; Raj, Bhiksha: On the Origin of Deep Learning. (2017) Xie, Qizhe ; Luong, Minh T. ; Hovy, Eduard ; Le, Quoc V.: Self-training with noisy student improves imagenet classification. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2020), p. 10684– 10695. – ISSN 10636919 Xie, Saining ; Girshick, Ross ; Dolla ́r, Piotr ; Tu, Zhuowen ; He, Kaiming: Aggre- gated residual transformations for deep neural networks. En: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Janua (2017), p. 5987–5995. ISBN 9781538604571 Xie, Y ; Zhang, J ; Xia, Y: Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT. En: Medical Image Analysis 57 (2019), p. 237–248 Xie, Yingpeng ; Wan, Qiwei ; Chen, Guozhen ; Xu, Yanwu ; B, Baiying L.: Retino- pathy Diagnosis Using Generative Adversarial Network. Vol. 1. Springer International Publishing. – 182–190 p.. – ISBN 9783030329563 Xu, Yan ; Mo, Tao ; Feng, Qiwei ; Zhong, Peilin ; Lai, Maode ; Chang, Eric I.: Deep learning of feature representation with multiple instance learning for medical image analysis. En: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2014), Nr. 1, p. 1626–1630. – ISBN 9781479928927 Yalniz, I. Z. ; Je ́gou, Herv ́e ; Chen, Kan ; Paluri, Manohar ; Mahajan, Dhruv: Billion-scale semi-supervised learning for image classification. (2019), 5 Yang, Xiangli ; Song, Zixing ; King, Irwin ; Xu, Zenglin: A Survey on Deep Semi- supervised Learning. (2021), 2 Zhang, Zhuo ; Srivastava, Ruchir ; Liu, Huiying ; Chen, Xiangyu ; Duan, Lixin ; Kee Wong, Damon W. ; Kwoh, Chee K. ; Wong, Tien Y. ; Liu, Jiang: A survey on computer aided diagnosis for ocular diseases. 14 (2014), p. 1–29. – ISSN 14726947 Zhao, Di ; Guallar, Eliseo ; Bowie, Janice V. ; Swenor, Bonnielin ; Gajwani, Prateek ; Kanwar, Natasha ; Friedman, David S.: Improving Follow-up and Reducing Barriers for Eye Screenings in Communities: The SToP Glaucoma Study. En: American Journal of Ophthalmology 188 (2018), p. 19–28. – ISSN 18791891 Zoph, Barret ; Vasudevan, Vijay ; Shlens, Jonathon ; Le, Quoc V.: Learning Trans- ferable Architectures for Scalable Image Recognition. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018), p. 8697–8710. – ISBN 9781538664209
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dc.format.extent.spa.fl_str_mv	viii, 35 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto0e9d70b5c1d7448338ca4467ccb27e59Perdomo Charry, Oscar Juliánc280ba13fd48e8dbf9cdbc8179aa9c94Arrieta Ramos, José Migueldd7a3994227ded26eb619482604084e5600Romero Castro, Edgar EduardoToledo Cortés, SantiagoMindlab2022-06-21T19:42:01Z2022-06-21T19:42:01Z2022-06-03https://repositorio.unal.edu.co/handle/unal/81620Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasRegular screening, early diagnosis, and appropriate on-time treatment could prevent vision loss and blindness as a complication of diabetes. Unfortunately, access to expert ophthal- mologists is limited and not readily available. Therefore, automated detection systems could improve access to specialized care by reducing screening time, cost, and e↵ort. Deep learning methods became popular for detecting ocular disease on eye fundus images because of their promising results. However, deep learning models need a large number of labeled images to learn, and the manual labeling of medical images results in a time-consuming and expensive process that requires medical experts in the retina, with little time to devote to this task. As a result, a limited number of annotated images are available. This thesis work proposes a semi-supervised method that leverages unlabeled images and labeled ones to train a mo- del that detects diabetic retinopathy via self-supervised pre-training followed by supervised fine-tuning and knowledge distillation with a small set of labeled images. This method was evaluated on the Messidor-2 dataset achieving 0.89 AUC using only 2 % EyePACS-Kaggle train labeled images.La pérdida de visión y ceguera como complicacíon de la diabetes se podrían prevenir con diagnóstico temprano, exámenes de deteccíon frequentes, y tratamiento oportuno adecuado. Desafortunadamente, el acceso a un oftalmólogo experto es limitado y no es fácilmente disponible. Es por esto que los sistemas de detección automatizados podrían mejorar el acceso a la atención especializada al reducir el tiempo, el costo y el esfuerzo para la detección. Los métodos de aprendizaje profundo se hicieron populares para la detección de enfermedades oculares en imágenes de fondo de ojo debido a sus buenos resultados. Sin embargo, los métodos de aprendizaje profundo necesitan una gran cantidad de imágenes etiquetadas para aprender, siendo el etiquetado manual de imágenes médicas un proceso costoso y lento que requiere escasos expertos médicos en la retina. Como resultado, el número de imágenes anotadas disponibles es limitado. Con este trabajo de tesis se propone un método semi-supervisado que aproveche las imágenes no etiquetadas además de las imágenes etiquetadas para entrenar un modelo que detecte la retinopatía diabética a través de aprendizaje auto-supervisado seguido de un ajuste fino supervisado y destilacion de conocimiento. Este método fue evaluado en el dataset de Messidor-2 logrando un AUC de 0.89 usando solamente 2 % de la particion de entrenamiento de EyePACS-Kaggle con imagenes etiquetadas. (Texto tomado de la fuente).MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónIntelligent systemsviii, 35 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresDiabetic Retinopathy/diagnostic imagingDeep LearningMachine LearningRetinopatía Diabética/diagnóstico por imagenAprendizaje ProfundoAprendizaje AutomáticoSelf-supervised learningDiabetic retinopathyMedical imagingDeep learningSemi-supervised learningImágenes médicasAprendizaje profundoAprendizaje semi-supervisadoAprendizaje autosupervisadoRetinopatía diabéticaSemi-supervised deep learning for ocular image classificationAprendizaje profundo semi-supervisado para la clasificación de imágenes ocularesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlbarqouni, Shadi ; Baur, Christoph ; Achilles, Felix ; Belagiannis, Vasileios ; Demirci, Stefanie ; Navab, Nassir: AggNet: Deep Learning From Crowds for Mito- sis Detection in Breast Cancer Histology Images. En: IEEE Transactions on Medical Imaging 35 (2016), Nr. 5, p. 1313–1321. – ISSN 1558254XAmerica, Central ; Rico, Puerto ; Republic, Dominican: Diabetes in South Central America - 2021 Diabetes in South Central America - 2021. (2021)Asiri, Norah ; Hussain, Muhammad ; Al, Fadwa ; Alzaidi, Nazih: Deep learning ba- sed computer-aided diagnosis systems for diabetic retinopathy : A survey. En: Artificial Intelligence In Medicine 99 (2019), Nr. December 2018, p. 101701. – ISSN 0933–3657Azizi, Shekoofeh ; Mustafa, Basil ; Ryan, Fiona ; Beaver, Zachary ; Freyberg, Jan ; Deaton, Jonathan ; Loh, Aaron ; Karthikesalingam, Alan ; Kornblith, Simon ; Chen, Ting ; Natarajan, Vivek ; Norouzi, Mohammad: Big Self-Supervised Models Advance Medical Image Classification. , p. 1–19B, Kevis-kokitsi M. ; Pont-tuset, Jordi ; Arbel, Pablo: Deep Retinal Image Un- derstanding. 1 (2016), p. 140–148. ISBN 978–3–319–46722–1Beede, Emma ; Baylor, Elizabeth ; Hersch, Fred ; Iurchenko, Anna ; Wilcox, Lauren ; Raumviboonsuk, Dr. P. ; Vardoulakis, Laura: A Human-Centered Eva- luation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. En: CHI 2020 Paper (2020), p. 1–12. ISBN 9781450367080Bernardes, Rui: Optical Coherence Tomography. 2015. – ISBN 9783642274091Berthelot, David ; Oliver, Avital ; Carlini, Nicholas ; Goodfellow, Ian ; Raf- fel, Colin ; Papernot, Nicolas: MixMatch : A Holistic Approach to Semi-Supervised Learning. (2019), Nr. NeurIPS, p. 1–11Bhaskaranand, Malavika ; Ramachandra, Chaithanya ; Bhat, Sandeep ; Cua- dros, Jorge ; Nittala, Muneeswar G. ; Sadda, Srinivas R. ; Solanki, Kaushal: The value of automated diabetic retinopathy screening with the EyeArt system: A study of more than 100,000 consecutive encounters from people with diabetes. En: Diabetes Technology and Therapeutics 21 (2019), Nr. 11, p. 635–643. – ISSN 15578593Bock, Rudiger ; Meier, Jorg ; Nyul, Laszlo G. ; Hornegger, Joachim ; Michelson, Georg: Glaucoma risk index: Automated glaucoma detection from color fundus images. En: Medical Image Analysis 14 (2010), Nr. 3, p. 471–481. – ISSN 13618415Burlina, Philippe M. ; Joshi, Neil ; Pacheco, Katia D. ; Freund, David E. ; Kong, Jun ; Bressler, Neil M.: Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk among Patients with Age-Related Macular Degeneration. En: JAMA Ophthalmology 136 (2018), Nr. 12, p. 1359–1366. – ISSN 21686165Charry, Oscar Julian P. ; Arevalo, John ; Gonza ́lez, Fabio A.: Combining morpho- metric features and convolutional networks fusion for glaucoma diagnosis, SPIE-Intl Soc Optical Eng, 11 2017. – ISBN 9781510616332, p. 57Chaves, Levy ; Bissoto, Alceu ; Valle, Eduardo ; Avila, Sandra: An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis. (2021), Nr. Ic, p. 1–12Chen, Ting ; Kornblith, Simon ; Norouzi, Mohammad ; Hinton, Geo↵rey: A Simple Framework for Contrastive Learning of Visual Representations. (2019)Chen, Ting ; Kornblith, Simon ; Swersky, Kevin ; Norouzi, Mohammad ; Hin- ton, Geo↵rey: Big Self-Supervised Models are Strong Semi-Supervised Learners. (2020), Nr. NeurIPS, p. 1–18Chen, Xiangyu ; Xu, Yanwu ; Wong, Damon Wing K. ; Wong, Tien Y. ; Liu, Jiang: Glaucoma detection based on deep convolutional neural network, Institute of Electrical and Electronics Engineers Inc., 11 2015. – ISBN 9781424492718, p. 715–718Christopher, Mark ; Belghith, Akram ; Bowd, Christopher ; Proudfoot, Ja- mes A. ; Goldbaum, Michael H. ; Weinreb, Robert N. ; Girkin, Christopher A. ; Liebmann, Je↵rey M. ; Zangwill, Linda M.: Performance of Deep Learning Architec- tures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. En: Scientific Reports 8 (2018), 12. – ISSN 20452322Ciga, Ozan ; Xu, Tony ; Martel, Anne L.: Self supervised contrastive learning for digital histopathology. (2020), 11Cui, Lei ; Feng, Jun ; Yang, Lin: Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification. En: Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (2019), p. 2333–2337. ISBN 9781538654880De Fauw, Jerey ; Ledsam, Joseph R. ; Romera-Paredes, Bernardino ; Nikolov, Stanislav ; Tomasev, Nenad ; Blackwell, Sam ; Askham, Harry ; Glorot, Xavier ; O‘Donoghue, Brendan ; Visentin, Daniel ; et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. En: Nature Medicine 24 (2018), Nr. 9, p. 1342–1350. – ISBN 4159101801Deng, J ; Dong, W ; Socher, R ; Li, L.-J. ; Li, K ; Fei-Fei, L: ImageNet: A Large-Scale Hierarchical Image Database. En: CVPR09, 2009Diaz-Pinto, Andres ; Colomer, Adrian ; Naranjo, Valery ; Morales, Sandra ; Xu, Yanwu ; Frangi, Alejandro F.: Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. En: IEEE Transactions on Medical Imaging 38 (2019), p. 2211–2218Diaz-Pinto, Andres ; Morales, Sandra ; Naranjo, Valery ; Kohler, Thomas ; Mossi, Jose M. ; Navea, Amparo: CNNs for automatic glaucoma assessment using fundus images: An extensive validation. En: BioMedical Engineering Online 18 (2019), 3. – ISSN 1475925XF.A, Perdomo Charry O. J; Gozalez O.: A Systematic Review of Deep Learning Methods Applied to Ocular Images. 30 (2019), Nr. 1Farsiu, Sina ; Chiu, Stephanie J. ; O‘Connell, Rachelle V. ; Folgar, Francisco A. ;Yuan, Eric ; Izatt, Joseph A. ; Toth, Cynthia A.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. En: Ophthalmology 121 (2014), Nr. 1, p. 162–172. – ISSN 01616420Fukushima, Kunihiko: Neocognitron: A hierarchical neural network capable of visual pattern recognition. En: Neural Networks 1 (1988), Nr. 2, p. 119–130. – ISSN 08936080Gargeya, Rishab ; Leng, Theodore: Automated Identification of Diabetic Retino- pathy Using Deep Learning. En: Ophthalmology 124 (2017), Nr. 7, p. 962–969. – ISSN 15494713Ghamdi, Manal A. ; Li, Mingqi ; mottaleb Mohamed, Mohamed A. ; Shousha, Abou: Semi-supervised Transfer Learning For Convolutional Neural Networks For Glau- coma Detection. ISBN 9781538646588Gholami, Peyman: Developing algorithms for the analysis of retinal Optical Coherence Tomography images. (2018)Grigg, Tom G. ; Busbridge, Dan ; Ramapuram, Jason ; Webb, Russ: Do Self- Supervised and Supervised Methods Learn Similar Visual Representations? (2021)Gulshan, Varun ; Peng, Lily ; Coram, Marc ; Stumpe, Martin C. ; Wu, Derek ; Narayanaswamy, Arunachalam ; Venugopalan, Subhashini ; Widner, Kasumi ; Madams, Tom ; Cuadros, Jorge ; Kim, Ramasamy ; Raman, Rajiv ; Nelson,Philip C. ; Mega, Jessica L. ; Webster, Dale R.: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. 94043 (2016), p. 1–9Hansen, Colin B. ; Nath, Vishwesh ; Gao, Riqiang ; Bermudez, Camilo ; Huo, Yuankai ; Sandler, Kim L. ; Massion, Pierre P. ; Blume, Je↵rey D. ; Lasko, Tho- mas A. ; Landman, Bennett A.: Semi-supervised Machine Learning with MixMatch and Equivalence Classes. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12446 LNCS (2020), p. 112–121. – ISBN 9783030611651Hervella, Alvaro S. ; Rouco, Jos ́e ; Novo, Jorge ; Ortega, Marcos: Self-supervised multimodal reconstruction of retinal images over paired datasets. En: Expert Systems with Applications 161 (2020). – ISSN 09574174Hinton, Geoffrey ; Vinyals, Oriol ; Dean, Jeff: Distilling the Knowledge in a Neural Network. (2015), p. 1–9de Jong, Paulus T.: Age-Related Macular Degeneration. (2006), p. 1474–1485Kaku, Aakash ; Upadhya, Sahana ; Razavian, Narges: Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning. (2021)Karen Simonyan, Andrew Z.: Very Deep Convolutional Networks For Large-Scale Image Recognition. (2015), p. 1–14Krause, Jonathan ; Gulshan, Varun ; Rahimy, Ehsan ; Karth, Peter ; Widner, Kasumi ; Corrado, Greg S. ; Peng, Lily ; Webster, Dale R.: Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. En: Ophthalmology 125 (2018), Nr. 8, p. 1264–1272. – ISSN 15494713Krizhevsky, Alex ; Hinton, Geo↵rey E.: ImageNet Classification with Deep Convo- lutional Neural Networks. , p. 1–9Lecun, Yann: 1.1 Deep Learning Hardware: Past, Present, and Future. (2019), p. 12–19. ISBN 9781538685310Lecun, Yann ; Bottou, Leon ; Bengio, Yoshua ; Ha, Patrick: Gradient-Based Lear- ning Applied to Document Recognition. (1998), Nr. November, p. 1–46Lee, Cecilia S. ; Baughman, Doug M. ; Lee, Aaron Y.: Deep Learning Is E↵ective for Classifying Normal versus Age-Related Macular Degeneration OCT Images. En: Kidney International Reports 1 (2017), Nr. 4, p. 322–327. – ISSN 24680249Lee, Dong-Hyun: Pseudo-label: The simple and e cient semi-supervised learning method for deep neural networks. En: ICML 2013 Workshop: Challenges in Repre- sentation Learning (2013), p. 1–6Litjens, Geert ; Kooi, Thijs ; Bejnordi, Babak E. ; Setio, Arnaud Arindra A. ; Ciompi, Francesco ; Ghafoorian, Mohsen ; van der Laak, Jeroen A W M. ; van Ginneken, Bram ; Sa ́nchez, Clara I.: A survey on deep learning in medical image analysis. En: Medical Image Analysis 42 (2017), Nr. December 2012, p. 60–88. – ISSN 13618423Liu, Jiamin ; Yao, Jianhua ; Bagheri, Mohammadhadi ; Summers, Ronald M.: A Semi-Supervised CNN Learning Method with Pseudo-class Labels for Atherosclerotic Vascular Calcification Detection. En: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (2019), p. 57Liu, Sijie ; B, Jingmin X. ; Wu, Jiayi ; Shi, Peiwen: Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening. (2019), p. 60–68. ISBN 9783030329563Mabu, Shingo ; Kido, Shoji ; Hirano, Yasushi ; Kuremoto, Takashi: Unsupervised and semi-supervised learning for e cient opacity annotation of di↵use lung diseases, 2019. – ISBN 9781510627758, p. 8Madani, 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, 2018, p. 1038–1042Marcus, Gary: Deep Learning: A Critical Appraisal. (2018), p. 1–27Moura, Joaquim D. ; Novo, Jorge ; Ortega, Marcos: Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema. 2019. – ISBN 9781728120096Organisation, World H.: World report on vision. 2019. 2019. – 1–160 p.. – ISBN 9789241516570Ota ́lora, Sebastian ; Marini, Niccolo ́ ; Muller, Henning ; Atzori, Manfredo: Semi- weakly Supervised Learning for Prostate Cancer Image Classification with Teacher- Student Deep Convolutional Networks. En: Lecture Notes in Computer Science (inclu- ding subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinforma- tics) 12446 LNCS (2020), p. 193–203. – ISBN 9783030611651Otalora Sebastian; Perdomo Oscar, Gonzalez Fabio ; Muller H.: Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images. En: MICCAI Workshop on Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS) 10552 (2017), p. 180–189. ISBN 978–3–319– 67533–6Perdomo, Oscar ; Otalora, Sebastian ; Gonzalez, Fabio A. ; Meriaudeau, Fabri- ce ; Muller, Henning: OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. En: Proceedings - Inter- national Symposium on Biomedical Imaging 2018-April (2018), Nr. Isbi, p. 1423–1426. – ISBN 9781538636367Perek, Shaked ; Amit, Mika ; Hexter, Efrat: Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance. Vol. 12928 LNCS. Springer International Publishing, 2021. – 117–127 p.. – ISBN 9783030876012Pooch, Eduardo H. ; Ballester, Pedro ; Barros, Rodrigo C.: Semi-supervised Classification of Chest Radiographs. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12446 LNCS (2020), p. 172–179. – ISBN 9783030611651Quellec, Gwenol ́e ; Charrie ́re, Katia ; Boudi, Yassine ; Cochener, B ́eatrice ; Lamard, Mathieu: Deep image mining for diabetic retinopathy screening. En: Medical Image Analysis 39 (2017), p. 178–193. – ISSN 13618423Rakhlin, Alexander: Diabetic Retinopathy detection through integration of Deep Learning classification framework. (2018), p. 1–11Ren, Kaiming He Xiangyu Zhang S.: Deep Residual Learning for Image Recognition. En: Enzyme and Microbial Technology 19 (2015), Nr. 2, p. 107–117. – ISBN 0141–0229Scale, International Clinical Diabetic Retinopathy Disease S.: International Clinical Diabetic Retinopathy. (2002), Nr. October, p. 8500Schmidhuber, Jurgen: Deep Learning in Neural Networks : An Overview. (2014), p. 1 – 88Sohn, Kihyuk ; Berthelot, David ; Zizhao, Chun-liang L. ; Nicholas, Zhang ; Cu- buk, Ekin D. ; Kurakin, Alex ; Zhang, Han ; Raffel, Colin: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidenc.Sun, Hong ; Saeedi, Pouya ; Karuranga, Suvi ; Pinkepank, Moritz ; Ogurtsova, Katherine ; Duncan, Bruce B. ; Stein, Caroline ; Basit, Abdul ; Chan, Juliana C. ; Mbanya, Jean C. ; Pavkov, Meda E. ; Ramachandaran, Ambady ; Wild, Sarah H. ; James, Steven ; Herman, William H. ; Zhang, Ping ; Bommer, Christian ; Kuo, Shihchen ; Boyko, Edward J. ; Magliano, Dianna J.: IDF Diabetes Atlas: Global,Sun, Wenqing ; Tseng, Tzu Liang B. ; Zhang, Jianying ; Qian, Wei: Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. En: Computerized Medical Imaging and Graphics 57 (2017), p. 4–9. – ISSN 18790771Suzuki, Kenji: Overview of deep learning in medical imaging. En: Radiological Physics and Technology 10 (2017), Nr. 3, p. 257–273. – ISSN 18650341Tarvainen, Antti ; Valpola, Harri: Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results. En: Advan- ces in Neural Information Processing Systems 2017-December (2017), p. 1196–1205. – ISSN 10495258Toledo-cort, Santiago ; Pava, Melissa De L. ; Perd, Oscar: Diabetic Retinopathy Diagnosis and Uncertainty.Van Tulder, Gijs ; De Bruijne, Marleen: Combining Generative and Discrimi- native Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines. En: IEEE Transactions on Medical Imaging 35 (2016), Nr. 5, p. 1262–1272. – ISSN 1558254XVoets, Mike ; Kajsa, Mollersen ; Bongo, Lars: Reproduction study using public data of : Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. (2019), p. 1–11Vu, Yen Nhi T. ; Wang, Richard ; Balachandar, Niranjan ; Liu, Can ; Ng, An- drew Y. ; Rajpurkar, Pranav: MedAug: Contrastive learning leveraging patient me- tadata improves representations for chest X-ray interpretation. (2021), p. 1–14Wang, Haohan ; Raj, Bhiksha: On the Origin of Deep Learning. (2017)Xie, Qizhe ; Luong, Minh T. ; Hovy, Eduard ; Le, Quoc V.: Self-training with noisy student improves imagenet classification. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2020), p. 10684– 10695. – ISSN 10636919Xie, Saining ; Girshick, Ross ; Dolla ́r, Piotr ; Tu, Zhuowen ; He, Kaiming: Aggre- gated residual transformations for deep neural networks. En: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Janua (2017), p. 5987–5995. ISBN 9781538604571Xie, Y ; Zhang, J ; Xia, Y: Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT. En: Medical Image Analysis 57 (2019), p. 237–248Xie, Yingpeng ; Wan, Qiwei ; Chen, Guozhen ; Xu, Yanwu ; B, Baiying L.: Retino- pathy Diagnosis Using Generative Adversarial Network. Vol. 1. Springer International Publishing. – 182–190 p.. – ISBN 9783030329563Xu, Yan ; Mo, Tao ; Feng, Qiwei ; Zhong, Peilin ; Lai, Maode ; Chang, Eric I.: Deep learning of feature representation with multiple instance learning for medical image analysis. En: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2014), Nr. 1, p. 1626–1630. – ISBN 9781479928927Yalniz, I. Z. ; Je ́gou, Herv ́e ; Chen, Kan ; Paluri, Manohar ; Mahajan, Dhruv: Billion-scale semi-supervised learning for image classification. (2019), 5Yang, Xiangli ; Song, Zixing ; King, Irwin ; Xu, Zenglin: A Survey on Deep Semi- supervised Learning. (2021), 2Zhang, Zhuo ; Srivastava, Ruchir ; Liu, Huiying ; Chen, Xiangyu ; Duan, Lixin ; Kee Wong, Damon W. ; Kwoh, Chee K. ; Wong, Tien Y. ; Liu, Jiang: A survey on computer aided diagnosis for ocular diseases. 14 (2014), p. 1–29. – ISSN 14726947Zhao, Di ; Guallar, Eliseo ; Bowie, Janice V. ; Swenor, Bonnielin ; Gajwani, Prateek ; Kanwar, Natasha ; Friedman, David S.: Improving Follow-up and Reducing Barriers for Eye Screenings in Communities: The SToP Glaucoma Study. En: American Journal of Ophthalmology 188 (2018), p. 19–28. – ISSN 18791891Zoph, Barret ; Vasudevan, Vijay ; Shlens, Jonathon ; Le, Quoc V.: Learning Trans- ferable Architectures for Scalable Image Recognition. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018), p. 8697–8710. – ISBN 9781538664209ORIGINAL1143362204.2022.pdf1143362204.2022.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf5715380https://repositorio.unal.edu.co/bitstream/unal/81620/3/1143362204.2022.pdf729696ee30585bf4f6f54fb45384503aMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81620/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1143362204.2022.pdf.jpg1143362204.2022.pdf.jpgGenerated Thumbnailimage/jpeg4248https://repositorio.unal.edu.co/bitstream/unal/81620/5/1143362204.2022.pdf.jpg96dd9be5f9f03f0c8fe35903f56226c4MD55unal/81620oai:repositorio.unal.edu.co:unal/816202023-08-05 23:04:01.885Repositorio Institucional Universidad Nacional de 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Semi-supervised deep learning for ocular image classification

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