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Ali Can
Researcher at General Electric
Publications - 83
Citations - 4880
Ali Can is an academic researcher from General Electric. The author has contributed to research in topics: Pixel & Image registration. The author has an hindex of 26, co-authored 83 publications receiving 4330 citations. Previous affiliations of Ali Can include Woods Hole Oceanographic Institution & ExxonMobil.
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Histopathological Image Analysis: A Review
TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.
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Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue
Michael J. Gerdes,Christopher J. Sevinsky,Anup Sood,Sudeshna Adak,Musodiq Bello,Alexander Bordwell,Ali Can,Corwin Alex D,Sean Richard Dinn,Robert John Filkins,Denise Hollman,Vidya Pundalik Kamath,Sireesha Kaanumalle,Kevin Bernard Kenny,Melinda Larsen,Michael Lazare,Qing Li,Christina Lowes,Colin Craig McCulloch,Elizabeth McDonough,Michael Christopher Montalto,Zhengyu Pang,Jens Rittscher,Alberto Santamaria-Pang,Brion Daryl Sarachan,Maximilian Lewis Seel,Antti Seppo,Kashan Shaikh,Yunxia Sui,Jingyu Zhang,Fiona Ginty +30 more
TL;DR: The results suggest MxIF should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.
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Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms
TL;DR: Its efficiency comes from direct processing on gray-level data without any preprocessing, and from processing only a minimally necessary fraction of pixels in an exploratory manner, avoiding low-level image-wide operations such as thresholding, edge detection, and morphological processing.
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A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina
TL;DR: A robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope, making the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions.
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Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy
Harihar Narasimha-Iyer,Ali Can,Badrinath Roysam,V. Stewart,H.L. Tanenbaum,A. Majerovics,Hanumant Singh +6 more
TL;DR: A fully automated approach to robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy, focusing on diabetic changes, has broader applicability in ophthalmology.