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dc.contributor.authorAl Mouiee, D
dc.contributor.authorMeijering, E
dc.contributor.authorKalloniatis, M
dc.contributor.authorNivison-Smith, L
dc.contributor.authorWilliams, RA
dc.contributor.authorNayagam, DAX
dc.contributor.authorSpencer, TC
dc.contributor.authorLuu, CD
dc.contributor.authorMcGowan, C
dc.contributor.authorEpp, SB
dc.contributor.authorShivdasani, MN
dc.date.accessioned2021-07-13T23:59:50Z
dc.date.available2021-07-13T23:59:50Z
dc.date.issued2021-06-01
dc.identifierpii: 2772685
dc.identifier.citationAl Mouiee, D., Meijering, E., Kalloniatis, M., Nivison-Smith, L., Williams, R. A., Nayagam, D. A. X., Spencer, T. C., Luu, C. D., McGowan, C., Epp, S. B. & Shivdasani, M. N. (2021). Classifying Retinal Degeneration in Histological Sections Using Deep Learning. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 10 (7), https://doi.org/10.1167/tvst.10.7.9.
dc.identifier.issn2164-2591
dc.identifier.urihttp://hdl.handle.net/11343/278473
dc.description.abstractPurpose: Artificial intelligence (AI) techniques are increasingly being used to classify retinal diseases. In this study we investigated the ability of a convolutional neural network (CNN) in categorizing histological images into different classes of retinal degeneration. Methods: Images were obtained from a chemically induced feline model of monocular retinal dystrophy and split into training and testing sets. The training set was graded for the level of retinal degeneration and used to train various CNN architectures. The testing set was evaluated through the best architecture and graded by six observers. Comparisons between model and observer classifications, and interobserver variability were measured. Finally, the effects of using less training images or images containing half the presentable context were investigated. Results: The best model gave weighted-F1 scores in the range 85% to 90%. Cohen kappa scores reached up to 0.86, indicating high agreement between the model and observers. Interobserver variability was consistent with the model-observer variability in the model's ability to match predictions with the observers. Image context restriction resulted in model performance reduction by up to 6% and at least one training set size resulted in a model performance reduction of 10% compared to the original size. Conclusions: Detecting the presence and severity of up to three classes of retinal degeneration in histological data can be reliably achieved with a deep learning classifier. Translational Relevance: This work lays the foundations for future AI models which could aid in the evaluation of more intricate changes occurring in retinal degeneration, particularly in other types of clinically derived image data.
dc.languageEnglish
dc.publisherASSOC RESEARCH VISION OPHTHALMOLOGY INC
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleClassifying Retinal Degeneration in Histological Sections Using Deep Learning
dc.typeJournal Article
dc.identifier.doi10.1167/tvst.10.7.9
melbourne.affiliation.departmentBiomedical Engineering
melbourne.affiliation.departmentOphthalmology (Eye & Ear Hospital)
melbourne.affiliation.facultyEngineering and Information Technology
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titleTranslational Vision Science and Technology
melbourne.source.volume10
melbourne.source.issue7
melbourne.identifier.nhmrc1063093
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1544399
melbourne.contributor.authorLuu, Chi
melbourne.contributor.authorSpencer, Thomas
dc.identifier.eissn2164-2591
melbourne.identifier.fundernameidNHMRC, 1063093
melbourne.accessrightsOpen Access


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