P
Péter Pollner
Researcher at Eötvös Loránd University
Publications - 77
Citations - 1573
Péter Pollner is an academic researcher from Eötvös Loránd University. The author has contributed to research in topics: Complex network & Medicine. The author has an hindex of 12, co-authored 67 publications receiving 1222 citations. Previous affiliations of Péter Pollner include Hungarian Academy of Sciences & Semmelweis University.
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Detecting and classifying lesions in mammograms with Deep Learning
TL;DR: In this article, the Faster R-CNN-based CAD system was proposed to detect malignant or benign lesions on a mammogram without any human intervention, which achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.95.
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Detecting and classifying lesions in mammograms with Deep Learning
TL;DR: A CAD system based on one of the most successful object detection frameworks, Faster R-CNN, that detects and classifies malignant or benign lesions on a mammogram without any human intervention is proposed.
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Directed network modules
TL;DR: It is found that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules.
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Preferential attachment of communities: The same principle, but a higher level
TL;DR: In this paper, the authors study how the links between communities are born in a growing co-authorship network, and introduce a simple model for the dynamics of overlapping communities, showing that the development of this modular structure is driven by preferential attachment, in complete analogy with the growth of the underlying network.
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Fundamental statistical features and self-similar properties of tagged networks
TL;DR: This work investigates the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes and introduces a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness and tag-Assortativity exponent.