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Clinical Ophthalmology: A Systematic Approach
TLDR
Ocular side-effects of systemic medication 21.1.Abstract:
1. Eyelids 2. Lacrimal Drainage System 3. Orbit 4. Dry Eye Disorders 5. Conjunctiva 6. Cornea 7. Corneal and Refractive Surgery 8. Episclera and Sclera 9. Lens 10. Glaucoma 11. Uveitis 12. Ocular Tumours 13. Retinal Vascular Disease 14. Acquired Macular Disorders 15. Hereditary Fundus Dystrophies 16. Retinal Detachment 17. Vitreous Opacities 18. Strabismus 19. Neuro-ophthalmology 20. Ocular side-effects of systemic medication 21. Trauma Indexread more
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Journal ArticleDOI
Impact of Macular Edema on Visual Acuity in Uveitis
TL;DR: Poor visual acuity in patients with CME was associated with the advanced age of the patients, chronic inflammation, and various specific uveitis entities and underlines the need for improved management of this complication.
Proceedings ArticleDOI
A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
Avijit Dasgupta,Sonam Singh +1 more
TL;DR: In this paper, the authors formulated the segmentation task as a multi-label inference task and utilized the implicit advantages of the combination of convolutional neural networks and structured prediction.
Journal ArticleDOI
Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning
TL;DR: This study proposes a system to automatically learn features for grading the severity of nuclear cataracts from slit-lamp images and proves it outperforms the state of the art by yielding with respect to clinical grading a mean absolute error of 0.304.
Biological and medical physics, biomedical engineering
TL;DR: The Biological and Medical Physics, Biomedical Engineering Series is intended to be comprehensive, covering a broad range of topics important to the study of the physical, chemical and biological sciences.
Book ChapterDOI
Dual Encoding U-Net for Retinal Vessel Segmentation
Bo Wang,Shuang Qiu,Huiguang He +2 more
TL;DR: A novel Dual Encoding U-Net (DEU-Net), which has two encoders: a spatial path with large kernel to preserve the spatial information and a context path with multiscale convolution block to capture more semantic information, and a feature fusion module to combine the different level of feature representation.