D
Dorin Comaniciu
Researcher at Princeton University
Publications - 632
Citations - 43059
Dorin Comaniciu is an academic researcher from Princeton University. The author has contributed to research in topics: Segmentation & Object detection. The author has an hindex of 74, co-authored 622 publications receiving 40541 citations. Previous affiliations of Dorin Comaniciu include Siemens & Rutgers University.
Papers
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Proceedings ArticleDOI
Bimodal system for interactive indexing and retrieval of pathology images
TL;DR: The prototype of an image understanding based system to support decision making in clinical pathology is demonstrated, employing all four major low level vision queues in content-based retrieval of visual information.
Journal ArticleDOI
A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.
David J Winkel,Angela Tong,Bin Lou,Ali Kamen,Dorin Comaniciu,Jonathan A. Disselhorst,Alejandro Rodriguez-Ruiz,Henkjan J. Huisman,Dieter H. Szolar,Ivan Shabunin,Moon Hyung Choi,Pengyi Xing,Tobias Penzkofer,Robert Grimm,Heinrich von Busch,Daniel T. Boll +15 more
TL;DR: In this paper, the authors evaluated the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.
Patent
System and method for detection of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree
TL;DR: In this article, a method for detecting fetal anatomic features in ultrasound images is proposed, which uses a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes.
Patent
System and method for detecting a passing vehicle from dynamic background using robust information fusion
TL;DR: In this article, a system and method for detecting a passing vehicle is disclosed, where image intensity and motion estimation are used to determine if the background dynamics has been violated in a given image frame.
Book ChapterDOI
Real-Time Multi-model Tracking of Myocardium in Echocardiography Using Robust Information Fusion
TL;DR: This work presents a unified framework for tracking the myocardium wall motion in real time with uncertainty handling and robust information fusion, which fully exploits uncertainties from the measurement, shape priors, motion dynamics, and matching process based on multiple appearance models.