V
Vasudevan Lakshminarayanan
Researcher at University of Waterloo
Publications - 366
Citations - 4472
Vasudevan Lakshminarayanan is an academic researcher from University of Waterloo. The author has contributed to research in topics: Image segmentation & Deep learning. The author has an hindex of 28, co-authored 353 publications receiving 3435 citations. Previous affiliations of Vasudevan Lakshminarayanan include Johns Hopkins University & Ryerson University.
Papers
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Explainable Deep Learning Models in Medical Image Analysis.
TL;DR: A review of the current applications of explainable deep learning for different medical imaging tasks is presented in this paper, where various approaches, challenges for clinical deployment, and the areas requiring further research are discussed from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.
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Zernike polynomials: a guide
TL;DR: In this paper, a special set of orthonormal functions, namely Zernike polynomials, which are widely used in representing the aberrations of optical systems are reviewed.
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Parametric representation of Stiles–Crawford functions: normal variation of peak location and directionality
TL;DR: Large-sample norms for foveal SCE peak location and spread are reported, various mathematical forms used for the empirical description of SCE data sets are discussed, and these norms are compared with values determined in other laboratories.
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Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey
TL;DR: This paper reviews segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate theDisc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images.
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Ophthalmic diagnosis using deep learning with fundus images - A critical review.
Sourya Sengupta,Sourya Sengupta,Amitojdeep Singh,Henry A. Leopold,Tanmay Gulati,Vasudevan Lakshminarayanan +5 more
TL;DR: An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented, and various retinal image datasets that can be used for deep learning purposes are described.