Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed due to the presence of clinical symptoms also related to pneumonia, meningitis, or others. Therefore, patients go untreated for these pathologies, resulting in death or neurological disability. It is essential to have a low-cost and high-specificity diagnostic technique for CM detection, for which We developed a method based on transfer learning (TL). Models pre-trained with TL select the good quality retinal images, which are fed into another TL model to detect CM. This approach shows a 96% specificity with low-cost retinal cameras.
Wydawca
Czasopismo
Rocznik
Tom
Strony
109--123
Opis fizyczny
Bibliogr. 78 poz., rys., tab., wykr.
Twórcy
autor
- Department of Electrical Engineering, The University of New Mexico, NM, USA
autor
- VisionQuest Biomedical Inc., Albuquerque, NM, USA
autor
- Benson AI, Wilmington, DE, USA
autor
- Department of Electrical Engineering, The University of New Mexico, NM, USA
autor
- VisionQuest Biomedical Inc., Albuquerque, NM, USA
autor
- VisionQuest Biomedical Inc., Albuquerque, NM, USA
Bibliografia
- [1] Seydel KB, Kampondeni SD, Valim C, Potchen MJ, Milner DA, Muwalo FW, et al. Brain swelling and death in children with cerebral malaria. N Engl J Med 2015;372(12):1126-37.
- [2] Taylor TE, Fu WJ, Carr RA, Whitten RO, Mueller JG, Fosiko NG, et al. Differentiating the pathologies of cerebral malaria by postmortem parasite counts. Nat Med 2004;10(2):143-5.
- [3] Birbeck GL, Beare N, Lewallen S, Glover SJ, Molyneux ME, Kaplan PW, et al. Identification of malaria retinopathy improves the specificity of the clinical diagnosis of cerebral malaria: findings from a prospective cohort study. Am J Trop Med Hygiene 2010;82(2):231.
- [4] Beare NA, Taylor TE, Harding SP, Lewallen S, Molyneux ME. Malarial retinopathy: a newly established diagnostic sign in severe malaria. Am J Trop Med Hygiene 2006;75(5):790-7.
- [5] Luzolo AL, Ngoyi DM. Cerebral malaria. Brain Res Bull 2019;145:53-8.
- [6] Albrecht-Schgoer K, Lackner P, Schmutzhard E, Baier G. Cerebral malaria: Current clinical and immunological aspects, Front Immunol 2022;13.
- [7] W.H. Organization. World malaria report 2020: 20 years of global progress and challenges.
- [8] Trampuz A, Jereb M, Muzlovic I, Prabhu RM. Clinical review: Severe malaria. Crit Care 2003;7(4):1-9.
- [9] Essuman VA, Ntim-Amponsah CT, Astrup BS, Adjei GO, Kurtzhals JA, Ndanu TA, et al. Retinopathy in severe malaria in ghanaian children-overlap between fundus changes in cerebral and non-cerebral malaria. Malaria J 2010;9(1):1-6.
- [10] W.H. Organization. World malaria report 2021.
- [11] Rahi M, Sharma A. Malaria control initiatives that have the potential to be gamechangers in india’s quest for malaria elimination. Lancet Regional Health-Southeast Asia 2022: 100009.
- [12] Paquet-Durand F, Beck SC, Das S, Huber G, Chang L, Schubert T, et al. A retinal model of cerebral malaria. Sci Rep 2019;9 (1):1-15.
- [13] Nanfack CN, Bilong Y, Kagmeni G, Nathan NN, Bella LA. Malarial retinopathy in adult: A case report. Pan Afr Med J 2017;27.
- [14] Cabrera PR, Talavera IR, Castillo ML, Valls RA, Morales JL. Detachment of retinal pigment epithelium in retinopathy due to malaria. Archivos de la Sociedad Española de Oftalmología (English Edition) 2018;93(8):406-10.
- [15] Small DS, Taylor TE, Postels DG, Beare NA, Cheng J, MacCormick IJ, et al. Evidence from a natural experiment that malaria parasitemia is pathogenic in retinopathy-negative cerebral malaria. eLife 2017;6:e23699.
- [16] Villaverde C, Namazzi R, Shabani E, Park GS, Datta D, Hanisch B, et al. Retinopathy-positive cerebral malaria is associated with greater inflammation, blood-brain barrier breakdown, and neuronal damage than retinopathy-negative cerebral malaria. J Pediatric Infecti Dis Soc 2020;9(5):580-6.
- [17] White VA, Lewallen S, Beare NA, Molyneux ME, Taylor TE. Retinal pathology of pediatric cerebral malaria in Malawi. PLoS One 2009;4(1):e4317.
- [18] Barrera V, MacCormick IJC, Czanner G, Hiscott PS, White VA, Craig AG, et al. Neurovascular sequestration in paediatric P. falciparum malaria is visible clinically in the retina. eLife 2018;7:e32208.
- [19] Tu Z, Gormley J, Sheth V, Seydel KB, Taylor T, Beare N, et al. Cerebral malaria: insight into pathology from optical coherence tomography. Sci Rep 2021;11(1):1-12.
- [20] Lewallen S, Beare NAV. Causes and Significance of Malarial Retinopathy. New York, New York, NY: Springer; 2014. p. 1-11.
- [21] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick IJ, et al. Automated detection of malarial retinopathy in digital fundus images for improved diagnosis in Malawian children with clinically defined cerebral malaria. Sci Rep 2017;7 (1):1-12.
- [22] MacCormick IJC, Zhang B, Hill D, Cordeiro MF, Small DS. A proposed theoretical framework for retinal biomarkers, Alzheimer’s & Dementia: Diagnosis. Assess Dis Monit 2022;14 (1):e12327.
- [23] Padhy SK, Sahu S, Govindahari V. Retinopathy secondary to uncomplicated plasmodium vivax malaria. Ophthalmic Surg, Lasers ImagRetina 2021;52(1):50-1.
- [24] MacCormick IJ, Beare NA, Taylor TE, Barrera V, White VA, Hiscott P, et al. Cerebral malaria in children: using the retina to study the brain. Brain 2014;137(8):2119-42.
- [25] Trivedi S, Chakravarty A. Neurological complications of malaria. Current Neurol Neurosci Rep 2022:1-15.
- [26] White VA, Lewallen S, Beare N, Kayira K, Carr RA, Taylor TE. Correlation of retinal haemorrhages with brain haemorrhages in children dying of cerebral malaria in Malawi. Trans R Soc Trop Med Hyg 2001;95(6):618-21.
- [27] MacCormick IJ, Barrera V, Beare NA, Czanner G, Potchen M, Kampondeni S, et al. How does blood-retinal barrier breakdown relate to death and disability in pediatric cerebral malaria? J Infect Dis 2022;225(6):1070-80.
- [28] White VA, Barrera V, MacCormick IJC. Ocular pathology of cerebral malaria. Malaria Immunology. Springer; 2022. p. 749-63.
- [29] Song X, Wei W, Cheng W, Zhu H, Wang W, Dong H, et al. Cerebral malaria induced by plasmodium falciparum: clinical features, pathogenesis, diagnosis, and treatment. Front Cell Infect Microbiol 2022:1033.
- [30] MacCormick IJC, Lewallen S, Beare N, Harding SP. Measuring the impact of malaria on the living human retina. Malaria Immunology. Springer; 2022. p. 731-48.
- [31] Nortey LN, Anning AS, Nakotey GK, Ussif AM, Opoku YK, Osei SA, et al. Genetics of cerebral malaria: pathogenesis, biomarkers and emerging therapeutic interventions. Cell Biosci 2022;12(1):1-19.
- [32] Singh J, Verma R, Tiwari A, Mishra D, Singh H. Retinopathy as a prognostic marker in cerebral malaria. Indian Pediatr 2016;53(4):315-7.
- [33] Swamy L, Beare NA, Okonkwo O, Mahmoud TH. Funduscopy in cerebral malaria diagnosis: An international survey of practice patterns. Am J Trop Med Hygiene 2018;98(2):516.
- [34] Kurup A, Soliz P, Nemeth S, Joshi V. Automated detection of malarial retinopathy using transfer learning. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). p. 18-21.
- [35] Beare NA, Harding SP, Taylor TE, Lewallen S, Molyneux ME. Perfusion abnormalities in children with cerebral malaria and malarial retinopathy. J Infect Dis 2009;199(2):263-71.
- [36] Zhao Y, MacCormick IJC, Parry DG, Leach S, Beare NAV, Harding SP, et al. Automated detection of leakage in fluorescein angiography images with application to malarial retinopathy. Sci Rep 2015;5(1):1-12.
- [37] Zhao Y, Zheng Y, Liu Y, Yang J, Zhao Y, Chen D, et al. Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy. IEEE Trans Med Imaging 2016;36(1):51-63.
- [38] Yan Q, Zhao Y, Zheng Y, Liu Y, Zhou K, Frangi A, et al. Automated retinal lesion detection via image saliency analysis. Med Phys 2019;46(10):4531-44.
- [39] Li W, Fang W, Wang J, He Y, Deng G, Ye H, et al. A weakly supervised deep learning approach for leakage detection in fluorescein angiography images. Transl Vision Sci Technol 2022;11(3). 9-9.
- [40] Joshi VS, Maude RJ, Reinhardt JM, Tang L, Garvin MK, Sayeed AA, et al. Automated detection of malarial retinopathy-associated retinal hemorrhages. Investigat Ophthalmol Visual Sci 2012;53(10):6582-8.
- [41] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick I, et al. Automated detection of retinal whitening in malarial retinopathy. In: Tourassi GD, S.G.A. III. editors, Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, International Society for Optics and Photonics, SPIE; 2016, p. 633 -39.
- [42] Joshi V, Wigdahl J, Nemeth S, Manda C, Lewallen S, Taylor T, et al. Automated detection of malarial retinopathy in retinal fundus images obtained in clinical settings. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2018. p. 5950-3.
- [43] Saleem M, Akram MU. Detection of hemorrhages for diagnosis of malarial retinopathy. In: 2014 Cairo International Biomedical Engineering Conference (CIBEC). p. 141-4.
- [44] Ashraf A, Akram MU, Sheikh SA, Abbas S. Detection of macular whitening and retinal hemorrhages for diagnosis of malarial retinopathy. In: 2015 IEEE International Conference on Imaging Systems and Techniques (IST). p. 1-5.
- [45] Son J, Shin JY, Kim HD, Jung K-H, Park KH, Park SJ. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 2020;127(1):85-94.
- [46] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision (IJCV) 2015;115(3):211-52.
- [47] Benson J, Carrillo H, Wigdahl J, Nemeth S, Maynard J, Zamora G. Transfer learning for diabetic retinopathy. Medical Imaging 2018: Image Processing, vol. 10574. International Society for Optics and Photonics; 2018. p. 105741Z.
- [48] Guo Y, Wang Y, Yang H, Zhang J, Sun Q. Dual-attention efficientnet based on multi-view feature fusion for cervical squamous intraepithelial lesions diagnosis. Biocybernet Biomed Eng 2022;42(2):529-42.
- [49] Alnussairi MHD, _ Ibrahim AA. Malaria parasite detection using deep learning algorithms based on (cnns) technique. Comput Electr Eng 2022;103:108316.
- [50] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y. editors. 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
- [51] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
- [52] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Informat Process Syst 2012;25.
- [53] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 1-9.
- [54] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016. p. 2818-26.
- [55] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications, CoRR abs/1704.04861.
- [56] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 770-78.
- [57] Huang G, Liu Z, Weinberger KQ. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 2261-69.
- [58] Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 1251-58.
- [59] Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR. p. 6105-14.
- [60] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 1994;5(2):157-66.
- [61] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings; 2010. p. 249-56.
- [62] Yazar Z, Hanioglu S, Karakoc G, Gürsel E. Asteroid hyalosis. Eur J Ophthalmol 2001;11(1):57-61.
- [63] Maurer CR, Qi R, Raghavan V. A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 2003;25(2):265-70.
- [64] Salehinejad H, Valaee S, Dowdell T, Barfett J. Image augmentation using radial transform for training deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2018. p. 3016-20.
- [65] Gonzales RC, Fittes B. Gray-level transformations for interactive image enhancement. Mech Mach Theory 1977;12 (1):111-22.
- [66] McCallum A, Nigam K, et al. A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol. 752, 1998. p. 41-8.
- [67] Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugenics 1936;7(2):179-88.
- [68] Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 1992;46(3):175-85.
- [69] Fix E, Hodges JL. Discriminatory analysis. nonparametric discrimination: Consistency properties. Int Stat Rev/Revue Int de Statistique 1989;57(3):238-47.
- [70] Kamiński B, Jakubczyk M, Szufel P. A framework for sensitivity analysis of decision trees. CEJOR 2018;26(1):135-59.
- [71] Breiman L. Bagging predictors. Mach Learn 1996;24(2):123-40. [72] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273-97.
- [73] Wold S, Sjöström M, Eriksson L. Pls-regression: a basic tool of chemometrics. Chemom Intel Lab Syst 2001;58(2):109-30.
- [74] Lindgren F, Geladi P, Wold S. The kernel algorithm for PLS. J Chemom 1993;7(1):45-59.
- [75] Breiman L. Random forests. Mach Learn 2001;45(1):5-32.
- [76] Tieleman T, Hinton G. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw Mach Learn 2012;4(2):26-31.
- [77] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 2000;28(2):337-407.
- [78] MacCormick IJ, Maude RJ, Beare NA, Borooah S, Glover S, Parry D, et al. Grading fluorescein angiograms in malarial retinopathy. Malaria J 2015;14(1):1-20.
- [1] Seydel KB, Kampondeni SD, Valim C, Potchen MJ, Milner DA, Muwalo FW, et al. Brain swelling and death in children with cerebral malaria. N Engl J Med 2015;372(12):1126-37.
- [2] Taylor TE, Fu WJ, Carr RA, Whitten RO, Mueller JG, Fosiko NG, et al. Differentiating the pathologies of cerebral malaria by postmortem parasite counts. Nat Med 2004;10(2):143-5.
- [3] Birbeck GL, Beare N, Lewallen S, Glover SJ, Molyneux ME, Kaplan PW, et al. Identification of malaria retinopathy improves the specificity of the clinical diagnosis of cerebral malaria: findings from a prospective cohort study. Am J Trop Med Hygiene 2010;82(2):231.
- [4] Beare NA, Taylor TE, Harding SP, Lewallen S, Molyneux ME. Malarial retinopathy: a newly established diagnostic sign in severe malaria. Am J Trop Med Hygiene 2006;75(5):790-7.
- [5] Luzolo AL, Ngoyi DM. Cerebral malaria. Brain Res Bull 2019;145:53-8.
- [6] Albrecht-Schgoer K, Lackner P, Schmutzhard E, Baier G. Cerebral malaria: Current clinical and immunological aspects, Front Immunol 2022;13.
- [7] W.H. Organization. World malaria report 2020: 20 years of global progress and challenges.
- [8] Trampuz A, Jereb M, Muzlovic I, Prabhu RM. Clinical review: Severe malaria. Crit Care 2003;7(4):1-9.
- [9] Essuman VA, Ntim-Amponsah CT, Astrup BS, Adjei GO, Kurtzhals JA, Ndanu TA, et al. Retinopathy in severe malaria in ghanaian children-overlap between fundus changes in cerebral and non-cerebral malaria. Malaria J 2010;9(1):1-6.
- [10] W.H. Organization. World malaria report 2021.
- [11] Rahi M, Sharma A. Malaria control initiatives that have the potential to be gamechangers in india’s quest for malaria elimination. Lancet Regional Health-Southeast Asia 2022: 100009.
- [12] Paquet-Durand F, Beck SC, Das S, Huber G, Chang L, Schubert T, et al. A retinal model of cerebral malaria. Sci Rep 2019;9 (1):1-15.
- [13] Nanfack CN, Bilong Y, Kagmeni G, Nathan NN, Bella LA. Malarial retinopathy in adult: A case report. Pan Afr Med J 2017;27.
- [14] Cabrera PR, Talavera IR, Castillo ML, Valls RA, Morales JL. Detachment of retinal pigment epithelium in retinopathy due to malaria. Archivos de la Sociedad Española de Oftalmología (English Edition) 2018;93(8):406-10.
- [15] Small DS, Taylor TE, Postels DG, Beare NA, Cheng J, MacCormick IJ, et al. Evidence from a natural experiment that malaria parasitemia is pathogenic in retinopathy-negative cerebral malaria. eLife 2017;6:e23699.
- [16] Villaverde C, Namazzi R, Shabani E, Park GS, Datta D, Hanisch B, et al. Retinopathy-positive cerebral malaria is associated with greater inflammation, blood-brain barrier breakdown, and neuronal damage than retinopathy-negative cerebral malaria. J Pediatric Infecti Dis Soc 2020;9(5):580-6.
- [17] White VA, Lewallen S, Beare NA, Molyneux ME, Taylor TE. Retinal pathology of pediatric cerebral malaria in Malawi. PLoS One 2009;4(1):e4317.
- [18] Barrera V, MacCormick IJC, Czanner G, Hiscott PS, White VA, Craig AG, et al. Neurovascular sequestration in paediatric P. falciparum malaria is visible clinically in the retina. eLife 2018;7:e32208.
- [19] Tu Z, Gormley J, Sheth V, Seydel KB, Taylor T, Beare N, et al. Cerebral malaria: insight into pathology from optical coherence tomography. Sci Rep 2021;11(1):1-12.
- [20] Lewallen S, Beare NAV. Causes and Significance of Malarial Retinopathy. New York, New York, NY: Springer; 2014. p. 1-11.
- [21] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick IJ, et al. Automated detection of malarial retinopathy in digital fundus images for improved diagnosis in Malawian children with clinically defined cerebral malaria. Sci Rep 2017;7 (1):1-12.
- [22] MacCormick IJC, Zhang B, Hill D, Cordeiro MF, Small DS. A proposed theoretical framework for retinal biomarkers, Alzheimer’s & Dementia: Diagnosis. Assess Dis Monit 2022;14 (1):e12327.
- [23] Padhy SK, Sahu S, Govindahari V. Retinopathy secondary to uncomplicated plasmodium vivax malaria. Ophthalmic Surg, Lasers ImagRetina 2021;52(1):50-1.
- [24] MacCormick IJ, Beare NA, Taylor TE, Barrera V, White VA, Hiscott P, et al. Cerebral malaria in children: using the retina to study the brain. Brain 2014;137(8):2119-42.
- [25] Trivedi S, Chakravarty A. Neurological complications of malaria. Current Neurol Neurosci Rep 2022:1-15.
- [26] White VA, Lewallen S, Beare N, Kayira K, Carr RA, Taylor TE. Correlation of retinal haemorrhages with brain haemorrhages in children dying of cerebral malaria in Malawi. Trans R Soc Trop Med Hyg 2001;95(6):618-21.
- [27] MacCormick IJ, Barrera V, Beare NA, Czanner G, Potchen M, Kampondeni S, et al. How does blood-retinal barrier breakdown relate to death and disability in pediatric cerebral malaria? J Infect Dis 2022;225(6):1070-80.
- [28] White VA, Barrera V, MacCormick IJC. Ocular pathology of cerebral malaria. Malaria Immunology. Springer; 2022. p. 749-63.
- [29] Song X, Wei W, Cheng W, Zhu H, Wang W, Dong H, et al. Cerebral malaria induced by plasmodium falciparum: clinical features, pathogenesis, diagnosis, and treatment. Front Cell Infect Microbiol 2022:1033.
- [30] MacCormick IJC, Lewallen S, Beare N, Harding SP. Measuring the impact of malaria on the living human retina. Malaria Immunology. Springer; 2022. p. 731-48.
- [31] Nortey LN, Anning AS, Nakotey GK, Ussif AM, Opoku YK, Osei SA, et al. Genetics of cerebral malaria: pathogenesis, biomarkers and emerging therapeutic interventions. Cell Biosci 2022;12(1):1-19.
- [32] Singh J, Verma R, Tiwari A, Mishra D, Singh H. Retinopathy as a prognostic marker in cerebral malaria. Indian Pediatr 2016;53(4):315-7.
- [33] Swamy L, Beare NA, Okonkwo O, Mahmoud TH. Funduscopy in cerebral malaria diagnosis: An international survey of practice patterns. Am J Trop Med Hygiene 2018;98(2):516.
- [34] Kurup A, Soliz P, Nemeth S, Joshi V. Automated detection of malarial retinopathy using transfer learning. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). p. 18-21.
- [35] Beare NA, Harding SP, Taylor TE, Lewallen S, Molyneux ME. Perfusion abnormalities in children with cerebral malaria and malarial retinopathy. J Infect Dis 2009;199(2):263-71.
- [36] Zhao Y, MacCormick IJC, Parry DG, Leach S, Beare NAV, Harding SP, et al. Automated detection of leakage in fluorescein angiography images with application to malarial retinopathy. Sci Rep 2015;5(1):1-12.
- [37] Zhao Y, Zheng Y, Liu Y, Yang J, Zhao Y, Chen D, et al. Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy. IEEE Trans Med Imaging 2016;36(1):51-63.
- [38] Yan Q, Zhao Y, Zheng Y, Liu Y, Zhou K, Frangi A, et al. Automated retinal lesion detection via image saliency analysis. Med Phys 2019;46(10):4531-44.
- [39] Li W, Fang W, Wang J, He Y, Deng G, Ye H, et al. A weakly supervised deep learning approach for leakage detection in fluorescein angiography images. Transl Vision Sci Technol 2022;11(3). 9-9.
- [40] Joshi VS, Maude RJ, Reinhardt JM, Tang L, Garvin MK, Sayeed AA, et al. Automated detection of malarial retinopathy-associated retinal hemorrhages. Investigat Ophthalmol Visual Sci 2012;53(10):6582-8.
- [41] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick I, et al. Automated detection of retinal whitening in malarial retinopathy. In: Tourassi GD, S.G.A. III. editors, Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, International Society for Optics and Photonics, SPIE; 2016, p. 633 -39.
- [42] Joshi V, Wigdahl J, Nemeth S, Manda C, Lewallen S, Taylor T, et al. Automated detection of malarial retinopathy in retinal fundus images obtained in clinical settings. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2018. p. 5950-3.
- [43] Saleem M, Akram MU. Detection of hemorrhages for diagnosis of malarial retinopathy. In: 2014 Cairo International Biomedical Engineering Conference (CIBEC). p. 141-4.
- [44] Ashraf A, Akram MU, Sheikh SA, Abbas S. Detection of macular whitening and retinal hemorrhages for diagnosis of malarial retinopathy. In: 2015 IEEE International Conference on Imaging Systems and Techniques (IST). p. 1-5.
- [45] Son J, Shin JY, Kim HD, Jung K-H, Park KH, Park SJ. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 2020;127(1):85-94.
- [46] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision (IJCV) 2015;115(3):211-52.
- [47] Benson J, Carrillo H, Wigdahl J, Nemeth S, Maynard J, Zamora G. Transfer learning for diabetic retinopathy. Medical Imaging 2018: Image Processing, vol. 10574. International Society for Optics and Photonics; 2018. p. 105741Z.
- [48] Guo Y, Wang Y, Yang H, Zhang J, Sun Q. Dual-attention efficientnet based on multi-view feature fusion for cervical squamous intraepithelial lesions diagnosis. Biocybernet Biomed Eng 2022;42(2):529-42.
- [49] Alnussairi MHD, _ Ibrahim AA. Malaria parasite detection using deep learning algorithms based on (cnns) technique. Comput Electr Eng 2022;103:108316.
- [50] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y. editors. 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
- [51] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
- [52] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Informat Process Syst 2012;25.
- [53] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 1-9.
- [54] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016. p. 2818-26.
- [55] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications, CoRR abs/1704.04861.
- [56] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 770-78.
- [57] Huang G, Liu Z, Weinberger KQ. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 2261-69.
- [58] Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 1251-58.
- [59] Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR. p. 6105-14.
- [60] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 1994;5(2):157-66.
- [61] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings; 2010. p. 249-56.
- [62] Yazar Z, Hanioglu S, Karakoc G, Gürsel E. Asteroid hyalosis. Eur J Ophthalmol 2001;11(1):57-61.
- [63] Maurer CR, Qi R, Raghavan V. A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 2003;25(2):265-70.
- [64] Salehinejad H, Valaee S, Dowdell T, Barfett J. Image augmentation using radial transform for training deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2018. p. 3016-20.
- [65] Gonzales RC, Fittes B. Gray-level transformations for interactive image enhancement. Mech Mach Theory 1977;12 (1):111-22.
- [66] McCallum A, Nigam K, et al. A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol. 752, 1998. p. 41-8.
- [67] Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugenics 1936;7(2):179-88.
- [68] Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 1992;46(3):175-85.
- [69] Fix E, Hodges JL. Discriminatory analysis. nonparametric discrimination: Consistency properties. Int Stat Rev/Revue Int de Statistique 1989;57(3):238-47.
- [70] Kamiński B, Jakubczyk M, Szufel P. A framework for sensitivity analysis of decision trees. CEJOR 2018;26(1):135-59.
- [71] Breiman L. Bagging predictors. Mach Learn 1996;24(2):123-40.
- [72] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273-97.
- [73] Wold S, Sjöström M, Eriksson L. Pls-regression: a basic tool of chemometrics. Chemom Intel Lab Syst 2001;58(2):109-30.
- [74] Lindgren F, Geladi P, Wold S. The kernel algorithm for PLS. J Chemom 1993;7(1):45-59.
- [75] Breiman L. Random forests. Mach Learn 2001;45(1):5-32.
- [76] Tieleman T, Hinton G. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw Mach Learn 2012;4(2):26-31.
- [77] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 2000;28(2):337-407.
- [78] MacCormick IJ, Maude RJ, Beare NA, Borooah S, Glover S, Parry D, et al. Grading fluorescein angiograms in malarial retinopathy. Malaria J 2015;14(1):1-20.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-974622f8-c339-458d-9aa5-8fb6e4227dc0