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Sharif Amit Kamran
Researcher at University of Nevada, Reno
Publications - 42
Citations - 371
Sharif Amit Kamran is an academic researcher from University of Nevada, Reno. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 5, co-authored 23 publications receiving 79 citations. Previous affiliations of Sharif Amit Kamran include Independent University, Bangladesh.
Papers
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Journal ArticleDOI
A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.
TL;DR: The proposed GAN produces anatomically accurate angiograms, with similar fidelity to FA images, and significantly outperforms two other state-of-the-art generative algorithms, and provides an unrivaled way for the translation of images from one domain to the other.
Proceedings ArticleDOI
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
TL;DR: A novel convolution neural network architecture is proposed to successfully distinguish between different degeneration of retinal layers and their underlying causes and predicts retinal diseases in real time while outperforming human diagnosticians.
Book ChapterDOI
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs Using a Novel Multi-scale Generative Adversarial Network
Sharif Amit Kamran,Khondker Fariha Hossain,Alireza Tavakkoli,Stewart Lee Zuckerbrod,Kenton M. Sanders,Salah A. Baker +5 more
TL;DR: Zhang et al. as mentioned in this paper proposed RV-GAN, a new multi-scale generative architecture for accurate retinal vessel segmentation to alleviate the loss of fidelity suffered by traditional GAN-based segmentation systems.
Book ChapterDOI
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
Sharif Amit Kamran,Khondker Fariha Hossain,Alireza Tavakkoli,Stewart Lee Zuckerbrod,Kenton M. Sanders,Salah A. Baker +5 more
TL;DR: RV-GAN as discussed by the authors uses two generators and two multi-scale autoencoding discriminators for better microvessel localization and segmentation, which achieves an area under the curve (AUC) of 0.9887, 0.9914, and 0.887 in pixel-wise segmentation of retinal vasculature.
Journal ArticleDOI
Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome
Joshua Ong,Ali Tavakkoli,Nasif Zaman,Sharif Amit Kamran,Ethan Waisberg,Nikhil Gautam,Andrew G. Lee +6 more
TL;DR: In this paper , the authors discuss the unique considerations for developing this technology for SANS and translational applications on Earth and discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology can help increase screening for early intervention.