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.
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Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
Florin C. Ghesu,Bogdan Georgescu,Awais Mansoor,Youngjin Yoo,Eli Gibson,R.S. Vishwanath,Abishek Balachandran,James M. Balter,Yue Cao,Ramandeep Singh,Subba R. Digumarthy,Mannudeep K. Kalra,Sasa Grbic,Dorin Comaniciu +13 more
TL;DR: It is demonstrated that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.
Patent
System and method for semantic indexing and navigation of volumetric images
TL;DR: In this article, a pre-trained classifier is used to segment the structure of interest from the image, and then a link is generated to the corresponding structure in the anatomical atlas.
Book ChapterDOI
Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach
Yang Wang,Bogdan Georgescu,Terrence Chen,Wen Wu,Peng Wang,Xiaoguang Lu,Razvan Ioan Ionasec,Yefeng Zheng,Dorin Comaniciu +8 more
TL;DR: This chapter presents a probabilistic framework that relies on anatomically indexed component-based object models which integrate several sources of information to determine the temporal trajectory of the deformable target and demonstrates various medical image analysis applications with focus on cardiology.
Proceedings ArticleDOI
Meanshift clustering for DNA microarray analysis
Danny Barash,Dorin Comaniciu +1 more
TL;DR: This work proposes implementing meanshift clustering to improve the efficiency of local mode seeking in analyzing expression data by implementing the meanshift vector that is derived by the formulation of the methodology.
Patent
System and method for detecting an object in a high dimensional space
TL;DR: In this article, a system and method for detecting an object in a high dimensional image space is disclosed, where a first classifier is trained in the marginal space of the object center location which generates a predetermined number of candidate object center locations.