Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares

  • Oscar Julian Perdomo Charry Universidad del Rosario
  • Fabio Augusto González Osorio Universidad Nacional de Colombia
Palabras clave: hallazgos clínicos, enfermedades oculares, bases de datos oculares, aprendizaje profundo, diagnóstico clínico

Resumen

La inteligencia artificial está teniendo un importante impacto en diversas áreas de la medicina y a la oftalmología no ha sido la excepción. En particular, los métodos de aprendizaje profundo han sido aplicados con éxito en la detección de signos clínicos y la clasificación de enfermedades oculares. Esto representa un potencial impacto en el incremento de pacientes correctamente y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente a imágenes de fondo de ojo y tomografía de coherencia óptica. Por un lado, estos métodos han logrado un rendimiento sobresaliente en la detección de enfermedades oculares tales como: retinopatía diabética, glaucoma, degeneración macular diabética y degeneración macular relacionada con la edad. Por otro lado, varios desafíos mundiales han compartido grandes conjuntos de datos con segmentación de parte de los ojos, signos clínicos y el diagnóstico ocular realizado por expertos. Adicionalmente, estos métodos están rompiendo el estigma de los modelos de caja negra, con la entrega de información clínica interpretable. Esta revisión proporciona una visión general de los métodos de aprendizaje profundo de última generación utilizados en imágenes oftálmicas, bases de datos y posibles desafíos para los diagnósticos oculares

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Referencias

Stitt et al. (2013). Advances in our understanding of diabetic retinopathy. Clinical science, 125(1), pp. 1-17. doi: 10.1042/CS20120588

Gurudath, N., Celenk, M., & Riley, H. B. (2014). Machine learning identification of diabetic retinopathy from fundus images. In 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1-7. doi: 10.1109/SPMB.2014.7002949

Priyadarshini, R., Dash, N., & Mishra, R. (2014). A Novel approach to predict diabetes mellitus using modified Extreme learning machine. In 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1-5. doi: 10.1109/ECS.2014.6892740

Quellec et al. (2011). Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Investigative ophthalmology & visual science, 52(11), pp. 8342-8348. doi: 10.1167/iovs.11-7418

Welikala et al. (2014). Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Computer methods and programs in biomedicine, 114(3), pp. 247-261. doi: 10.1016/j.cmpb.2014.02.010.

Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2013). DREAM: diabetic retinopathy analysis using machine learning. IEEE journal of biomedical and health informatics, 18(5), pp. 1717-1728. doi: 10.1109/JBHI.2013.2294635.

Usher et al. (2004). Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetic Medicine, 21(1), pp. 84-90. doi: 10.1046/j.1464-5491.2003.01085.x.

Philip et al. (2007). The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme. British Journal of Ophthalmology, 91(11), pp. 1512-1517. doi: 10.1136/bjo.2007.119453.

Cheng, S. C., & Huang, Y. M. (2003). A novel approach to diagnose diabetes based on the fractal characteristics of retinal images. IEEE Transactions on Information Technology in Biomedicine, 7(3), pp. 163-170. doi: 10.1109/TITB.2003.813792.

García et al. (2009). Neural network based detection of hard exudates in retinal images. Computer Methods and programs in biomedicine, 93(1), pp. 9-19. doi: 10.1016/j.cmpb.2008.07.006.

Lu et al. (2018). Applications of artificial intelligence in ophthalmology: general overview. Journal of ophthalmology, 2018. doi: 10.1155/2018/5278196.

Vandarkuzhali, D. C. S., & Ravichandran, T. (2005). Elm based detection of abnormality in retinal image of eye due to diabetic retinopathy. Journal of theoretical and applied information technology, 6, pp. 423-428.

Antal, B., & Hajdu, A. (2014). An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-based systems, 60, pp. 20-27. doi: 10.1016/j.knosys.2013.12.023.

Yoo, T. K., & Park, E. C. (2013). Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study. BMC medical informatics and decision making, 13(1), pp. 106. doi: 10.1186/1472-6947-13-106.

Cho et al. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 138, pp. 271-281. doi: 10.1016/j.diabres.2018.02.023.

International Diabetes Federation (IDF). IDF Diabetes Atlas 8th Edition. 2017. Available in: https://www.idf.org/e-library/epidemiology-research/diabetes-atlas.html (visited on 30/07/2019).

American Diabetes Association. (2019). 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2019. Diabetes Care, 42(Supplement 1), S13-S28. doi: 10.2337/dc19-S002.

Baker, C. W., Jiang, Y., & Stone, T. (2016). Recent advancements in diabetic retinopathy treatment from the Diabetic Retinopathy Clinical Research Network. Current opinion in ophthalmology, 27(3), pp. 210. doi: 10.1097/ICU.0000000000000262.

Yau et al. (2012). Global prevalence and major risk factors of diabetic retinopathy. Diabetes care, 35(3), pp. 556-564. doi: 10.2337/dc11-1909.

Guariguata et al. (2018). An updated systematic review and meta-analysis on the social determinants of diabetes and related risk factors in the Caribbean. Revista Panamericana de Salud Pública, 42. doi: 10.26633/RPSP.2018.171.

Zhang, et al. (2014). A survey on computer aided diagnosis for ocular diseases. BMC medical informatics and decision making, 14(1), pp. 80. doi: 10.1186/1472-6947-14-80.

Fleming, et al. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE transactions on medical imaging, 25(9), pp. 1223-1232. doi: 10.1109/TMI.2006.879953.

Porwal, et al. (2018). Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data, 3(3), pp. 25. doi: 10.3390/data3030025.

Kamble, et al. (2018). Automated diabetic macular edema (DME) analysis using fine tuning with Inception-Resnet-v2 on OCT images. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 442-446. doi: 10.1109/IECBES.2018.8626616.

Bernardes, R., & Cunha-Vaz, J. (Eds.). (2012). Optical coherence tomography: a clinical and technical update. Springer Science & Business Media. doi: 10.1007/978-3-642-27410-7.

Niemeijer et al. (2009). Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE transactions on medical imaging, 29(1), pp. 185-195. doi: 10.1109/TMI.2009.2033909.

Srinivasan et al. (2014). Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomedical optics express, 5(10), pp. 3568-3577. doi: 10.1364/BOE.5.003568.

Zhao, et al. (2018). Improving follow-up and reducing barriers for eye screenings in communities: the stop glaucoma study. American journal of ophthalmology, 188, pp. 19-28. doi: 10.1016/j.ajo.2018.01.008.

Mookiah, et al. (2012). Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-Based Systems, 33, pp. 73-82. doi: 10.1016/j.knosys.2012.02.010.

Bock, et al. (2010). Glaucoma risk index: automated glaucoma detection from color fundus images. Medical image analysis, 14(3), pp. 471-481. doi: 10.1016/j.media.2009.12.006.

Fumero, F., Alayón, S., Sanchez, J. L., Sigut, J., & Gonzalez-Hernandez, M. (2011, June). RIM-ONE: An open retinal image database for optic nerve evaluation. In 2011 24th international symposium on computer-based medical systems (CBMS), pp. 1-6. doi: 10.1109/CBMS.2011.5999143.

Maetschke, et al. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PloS one, 14(7), e0219126. doi: 10.1371/journal.pone.0219126.

De Jong, P. T. (2006). Age-related macular degeneration. New England Journal of Medicine, 355(14), pp. 1474-1485. doi: 10.1056/NEJMra062326

Huazhu F. et al. iChallenge-AMD (2019). [Online] http://ai.baidu.com.

Farsiu, et al. (2014). Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology, 121(1), pp. 162-172. doi: 10.1016/j.ophtha.2013.07.013.

Chen et al. (2012). Macular Thickness and Aging in Retinitis Pigmentosa. Optometry and Vision Science, 89(4), pp. 471-482. doi: 10.1097/OPX.0b013e31824c0b0b.

Mactier, H., Bradnam, M. S., & Hamilton, R. (2013). Dark-adapted oscillatory potentials in preterm infants with and without retinopathy of prematurity. Documenta Ophthalmologica, 127(1), pp. 33-40. doi: 10.1007/s10633-013-9373-2.

Dhamdhere et al. (2012). Associations between local retinal thickness and function in early diabetes. Investigative ophthalmology & visual science, 53(10), pp. 6122-6128. doi: 10.1167/iovs.12-10293.

Karlica et al. (2010). Visual evoked potential can be used to detect a prediabetic form of diabetic retinopathy in patients with diabetes mellitus type I. Collegium antropologicum, 34(2), pp. 525-529. doi: 10.18203/2320-6012.ijrms20151405.

Lövestam-Adrian et al. (2012). Multifocal visual evoked potentials (MFVEP) in diabetic patients with and without polyneuropathy. The open ophthalmology journal, 6, 98. doi: 10.2174/1874364101206010098.

Gupta et al. (2017). Electrophysiological evaluation in patients with type 2 diabetes mellitus by pattern reversal visual evoked potentials. National Journal of Physiology, Pharmacy and Pharmacology, 7(5), pp. 527. doi: 10.5455/njppp.2017.7.1235824012017.

Heravian et al. (2012). Pattern visual evoked potentials in patients with type II diabetes mellitus. Journal of ophthalmic & vision research, 7(3), 225.

Kardon et al. (2011). Chromatic pupillometry in patients with retinitis pigmentosa. Ophthalmology, 118(2), pp. 376-381. doi: 10.1016/j.ophtha.2010.06.033.

Ortube et al. (2013). Comparative regional pupillography as a noninvasive biosensor screening method for diabetic retinopathy. Investigative ophthalmology & visual science, 54(1), pp. 9-18. doi: 10.1167/iovs.12-10241.

Threatt et al. (2013). Ocular disease, knowledge and technology applications in patients with diabetes. The American journal of the medical sciences, 345(4), pp. 266-270. doi: 10.1097/MAJ.0b013e31828aa6fb.

Mitry et al. (2013). Crowdsourcing as a novel technique for retinal fundus photography classification: Analysis of Images in the EPIC Norfolk Cohort on behalf of the UKBiobank Eye and Vision Consortium. PloS one, 8(8), e71154. doi: 10.1371/journal.pone.0071154.

Staal et al. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4), pp. 501-509. doi: 10.1109/TMI.2004.825627.

Kauppi et al. (2006). DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, 73, pp. 1-17. doi: 10.1.1.128.4274.

Kauppi et al. (2007). The diaretdb1 diabetic retinopathy database and evaluation protocol. In BMVC (Vol. 1, pp. 1-10. doi: 10.5244/C.21.15.

Giancardo et al. (2011). Microaneurysm detection with radon transform-based classification on retina images. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5939-5942. doi: 10.1109/IEMBS.2011.6091562.

Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transactions on Biomedical Engineering, 59(9), pp. 2538-2548. doi: 10.1109/TBME.2012.2205687.

Decencière et al. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. Irbm, 34(2), pp. 196-203. doi: 10.1016/j.irbm.2013.01.010.

EyePACS Challenge. Diabetic retinopathy detection of Kaggle. Available in: https://www.kaggle.com/c/diabetic-retinopathy-detection/data

"APTOS 2019 BLINDNESS DETECTION". [Online] https://www.kaggle.com/c/aptos2019-blindness-detection/data

Lowell et al. (2004). Optic nerve head segmentation. IEEE Transactions on medical Imaging, 23(2), pp. 256-264. doi: 10.1109/TMI.2003.823261.

Budai et al (2013). Robust vessel segmentation in fundus images. International journal of biomedical imaging. doi: 10.1155/2013/154860.

Hoover, A., Kouznetsova, V., & Goldbaum, M. (1998). Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. In Proceedings of the AMIA Symposium, p. 931. American Medical Informatics Association. doi: 10.1109/42.845178.

Hoover, A., & Goldbaum, M. (2003). Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE transactions on medical imaging, 22(8), pp. 951-958. doi: 10.1109/TMI.2003.815900.

Farnell et al. (2008). Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. Journal of the Franklin institute, 345(7), pp. 748-765. doi: 10.1016/j.jfranklin.2008.04.009.

Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated “disease/no disease” grading of age-related macular degeneration by an image mining approach. Investigative ophthalmology & visual science, 53(13), pp. 8310-8318. doi: 10.1167/iovs.12-9576.

Gholami, P., Roy, P., Parthasarathy, M. K., & Lakshminarayanan, V. (2018). OCTID: Optical Coherence Tomography Image Database. arXiv preprint arXiv:1812.07056. doi: 10.5683/SP2/W43PFI.

Carmona et al. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), pp. 243-259. doi: 10.1016/j.artmed.2008.04.005.

Zhang et al. (2010). Origa-light: An online retinal fundus image database for glaucoma analysis and research. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 3065-3068. doi: 10.1109/IEMBS.2010.5626137.

Niemeijer et al. (2011). Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Transactions on medical imaging, 30(11), pp. 1941-1950. doi: 10.1109/TMI.2011.2159619.

Zhang et al. (2013). ACHIKO-K: Database of fundus images from glaucoma patients. In 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), pp. 228-231. doi: 10.1109/ICIEA.2013.6566371.

Sivaswamy et al. (2015). A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedical Imaging Data Papers, 2(1), 1004.

Sivaswamy et al. (2014). Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation. In 2014 IEEE 11th international symposium on biomedical imaging (ISBI), pp. 53-56. doi: 10.1109/ISBI.2014.6867807.

Almazroa et al. (2018). Retinal fundus images for glaucoma analysis: the RIGA dataset. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications (Vol. 10579, p. 105790B). International Society for Optics and Photonics. doi: 10.1117/12.2293584.

Huazhu et al. (2019). REFUGE: Retinal Fundus Glaucoma Challenge, IEEE Dataport, 2019. [Online]. doi: 10.21227/tz6e-r977.

Clemons et al. (2003). National Eye Institute visual function questionnaire in the age-related eye disease study (AREDS): AREDS report no. 10. Archives of Ophthalmology, 121(2), pp. 211-217. doi: 10.1001/archopht.121.2.211.

Jahromi et al. (2014). An automatic algorithm for segmentation of the boundaries of corneal layers in optical coherence tomography images using gaussian mixture model. Journal of medical signals and sensors, 4(3), pp. 171. doi: 10.4103/2228-7477.137763

Giancardo et al. (2012). Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical image analysis, 16(1), pp. 216-226. doi: 10.1016/j.media.2011.07.004.

Rasti et al. (2017). Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE transactions on medical imaging, 37(4), pp. 1024-1034. doi: 10.1109/TMI.2017.2780115.

Kermany et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), pp. 1122-1131. doi: 10.1016/j.cell.2018.02.010.

Paul, S., & Singh, L. (2015). A review on advances in deep learning. In 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pp. 1-6. doi: 10.1109/WCI.2015.7495514.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097-1105. doi: 10.1145/3065386.

Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision, pp. 818-833. doi: 10.1007/978-3-319-10590-1_53.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Szegedy et al. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. doi: 10.1109/CVPR.2015.7298594.

Hinton et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine, 29. doi: 10.1109/MSP.2012.2205597.

Abdel-Hamid et al. (2014). Convolutional neural networks for speech recognition. IEEE/ACM Transactions on audio, speech, and language processing, 22(10), pp. 1533-1545. doi: 10.1109/TASLP.2014.2339736.

Sainath et al. (2015). Deep convolutional neural networks for large-scale speech tasks. Neural Networks, 64, pp. 39-48. doi: 10.1016/j.neunet.2014.08.005.

Kaggle: Higgs boson machine learning challenge. Available in: http://www.kaggle.com/c/higgs-boson, September 2014.

Kaggle: 1000 Fundus images with 39 categories. Available in: https://www.kaggle.com/linchundan/fundusimage1000, July 2019.

de Brebisson, A., & Montana, G. (2015). Deep neural networks for anatomical brain segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20-28. doi: 10.1109/CVPRW.2015.7301312

Shin, H. C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2012). Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE transactions on pattern analysis and machine intelligence, 35(8), pp. 1930-1943. doi: 10.1109/TPAMI.2012.277.

The cancer genome atlas. Available in: http://www.cancerimagingarchive.net/.

Spineweb: Collaborative platform for research on spine imaging and image analysis. Available in: http://spineweb.digitalimaginggroup.ca/

Perdomo, et al. (2018). 3D deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes. In 14th International Symposium on Medical Information Processing and Analysis, vol. 10975, p. 109750I, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (Vol. 10975). doi: https://doi.org/10.1117/12.2511597.

Otálora, et al (2017). Training deep convolutional neural networks with active learning for exudate classification in eye fundus images. In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 146-154. Springer, Cham, 2017.

Szegedy et al. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826. doi: 10.1109/CVPR.2016.308.

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241. doi: 10.1007/978-3-319-24574-4_28.

Akram, M. U., Khalid, S., Tariq, A., Khan, S. A., & Azam, F. (2014). Detection and classification of retinal lesions for grading of diabetic retinopathy. Computers in biology and medicine, 45, pp. 161-171. doi: 10.1016/j.compbiomed.2013.11.014

Aujih et al. (2018). Analysis of retinal vessel segmentation with deep learning and its effect on diabetic retinopathy classification. In 2018 International conference on intelligent and advanced system (ICIAS), pp. 1-6. doi: 10.1109/ICIAS.2018.8540642.

Yang et al. (2017). Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 533-540. doi: 10.1007/978-3-319-66179-7_61.

Gao et al. (2018). Diagnosis of Diabetic Retinopathy Using Deep Neural Networks. IEEE Access, 7, pp. 3360-3370. doi: 10.1109/ACCESS.2018.2888639.

Quellec et al. (2017). Deep image mining for diabetic retinopathy screening. Medical image analysis, 39, pp. 178-193. doi: 10.1016/j.media.2017.04.012.

Gulshan et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), pp. 2402-2410. doi: 10.1001/jama.2016.17216.

Perdomo, O., Arevalo, J., & González, F. A. (2017). Convolutional network to detect exudates in eye fundus images of diabetic subjects. In 12th International Symposium on Medical Information Processing and Analysis (Vol. 10160, p. 101600T). International Society for Optics and Photonics. doi: 10.1117/12.2256939.

Perdomo et al. (2016). A novel machine learning model based on exudate localization to detect diabetic macular edema. In: Ophthalmic Medical Image Analysis Third International Workshop (OMIA), pp. 137-144. doi: 10.17077/omia.1057.

Wang et al. (2019). Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation. arXiv preprint arXiv:1902.07519.

Kumar, J. H., Pediredla, A. K., & Seelamantula, C. S. (2015). Active discs for automated optic disc segmentation. In 2015 IEEE global conference on signal and information processing (GlobalSIP) (pp. 225-229). IEEE.

Perdomo, O., Arevalo, J., & González, F. A. (2017). Combining morphometric features and convolutional networks fusion for glaucoma diagnosis. In 13th International Conference on Medical Information Processing and Analysis (Vol. 10572, p. 105721G). International Society for Optics and Photonics. doi: 10.1117/12.2285964.

Perdomo et al. (2018). Glaucoma diagnosis from eye fundus images based on deep morphometric feature estimation. In Computational pathology and ophthalmic medical image analysis, pp. 319-327. doi: 10.1007/978-3-030-00949-6_38.

Burlina et al. (2018). Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration. JAMA ophthalmology, 136(12), pp. 1359-1366. doi: 10.1001/jamaophthalmol.2018.4118.

Gholami, P. (2018). Developing algorithms for the analysis of retinal Optical Coherence Tomography images (Master's thesis, University of Waterloo).

Perdomo et al. (2018). Oct-net: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1423-1426. doi: 10.1109/ISBI.2018.8363839.

Sun, W., Liu, X., & Yang, Z. (2017). Automated detection of age-related macular degeneration in OCT images using multiple instance learning. In Ninth International Conference on Digital Image Processing (ICDIP 2017) (Vol. 10420, p. 104203V). International Society for Optics and Photonics. doi: 10.1117/12.2282522

Perdomo et al. (2019). Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Computer Methods and Programs in Biomedicine, 178, pp. 181-189. doi: 10.1016/j.cmpb.2019.06.016.

De Fauw et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine, 24(9), pp. 1342. doi: 10.1038/s41591-018-0107-6.

Lee, C. S., Baughman, D. M., & Lee, A. Y. (2017). Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmology Retina, 1(4), 322-327. doi: 10.1016/j.oret.2016.12.009.

He et al. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. doi: 10.1109/CVPR.2016.90.

Voets, M., Møllersen, K., & Bongo, L. A. (2018). Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. arXiv preprint arXiv:1803.04337. doi: 10.1371/journal.pone.0217541.

Cómo citar
Perdomo Charry, O. J., & González Osorio, F. A. (2019). Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares. Ciencia E Ingeniería Neogranadina, 30(1). https://doi.org/10.18359/rcin.4242
Publicado
2019-11-08
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Artículos