Vasudevan Lakshminarayanan

Bio: Vasudevan Lakshminarayanan is an academic researcher from University of Waterloo. The author has contributed to research in topics: Image segmentation & Engineering. 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.

Explainable Deep Learning Models in Medical Image Analysis.

Abstract: Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those However, the black-box nature of the algorithms has restricted their clinical use Recent explainability studies aim to show the features that influence the decision of a model the most The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations A review of the current applications of explainable deep learning for different medical imaging tasks is presented here The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users

Zernike polynomials: a guide

Abstract: In this paper we review a special set of orthonormal functions, namely Zernike polynomials which are widely used in representing the aberrations of optical systems. We give the recurrence relations, relationship to other special functions, as well as scaling and other properties of these important polynomials. Mathematica code for certain operations are given in the Appendix.

Parametric representation of Stiles–Crawford functions: normal variation of peak location and directionality

Abstract: Evidence suggests that the psychophysically determined Stiles-Crawford effect of the first kind (SCE) reflects waveguide properties of human photoreceptors. The peak of the SCE data set is assumed to reflect the principal alignment tendencies, and the spread (e.g., rho value, the curvature or width at half-height) is assumed to reflect the directionality (i.e., interreceptor differences in alignment) of the population of photoreceptors being tested. As such, disruption of the normal SCE can be used and/or has been used (1) to document early natural history of retinal pathology involving the photoreceptors, (2) to provide a firm rationale for therapeutic intervention, and (3) to provide a method for monitoring therapies designed to alter the natural course of retinal-disease processes. We report large-sample norms for foveal SCE peak location and spread (horizontal peak location, nasal 0.51 +/- 0.72, horizontal rho value 0.047 +/- 0.013, vertical peak location, superior 0.20 +/- 0.64, vertical rho value 0.053 +/- 0.012), compare these norms with values determined in other laboratories, and discuss the various mathematical forms used for the empirical description of SCE data sets.

Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

Abstract: Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc 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. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.

Computational modelling of visual attention.

Abstract: Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.