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Interactive Mining for Large-scale N...

Li, Zhongyu.

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Interactive Mining for Large-scale Neuro-morphological Datasets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interactive Mining for Large-scale Neuro-morphological Datasets./
Author:	Li, Zhongyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	97 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10840970
ISBN:	9780438254664

Interactive Mining for Large-scale Neuro-morphological Datasets.
Li, Zhongyu.

Interactive Mining for Large-scale Neuro-morphological Datasets. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 97 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.

In this dissertation, we aim to investigate advanced methods for the computational analytics of large-scale neuro-morphological datasets, which can help neuroscientists interactively explore neurons in real-time. Particularly, we tackle the neuro-morphological analytics into three inter-related components: 1) quantitative descriptions for 3D neuron morphologies, i.e., computing effective features that can differentiate subtle difference among massive neurons; 2) large-scale neuron mining, i.e., efficiently indexing neurons with similar morphologies in large-scale datasets; 3) interactively neuron exploration and visualization, i.e., developing neuron visualization tools and bring human in the loop to explore neurons in an interactive and immersive manner. We propose a series of methods in tackling problems related to the above three components. Regarding the quantitative description, we develop a deep learning framework based on neuron projection, which can transform 3D neurons into 2D images and learn effective neuron features. Regarding the large-scale neuron mining, binary coding methods are introduced, which can transform feature vectors into short binary codes for real-time indexing and mining. Regarding the interactive neuron exploration, we visualize 3D neurons using augmented reality (AR) techniques, where users can provide relevance feedback to further improve the mining performance. The proposed methods are validated on the currently largest neuron database including more than 58,000 neurons, achieving state-of-the-art performance in comparison with other related methods. More importantly, we demonstrate use cases of our framework in multiple neuron analysis and exploration tasks, showing its potential benefits in facilitating the research of neuroscience.

ISBN: 9780438254664Subjects--Topical Terms:

523869
Computer science.

Interactive Mining for Large-scale Neuro-morphological Datasets.
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520 $a In this dissertation, we aim to investigate advanced methods for the computational analytics of large-scale neuro-morphological datasets, which can help neuroscientists interactively explore neurons in real-time. Particularly, we tackle the neuro-morphological analytics into three inter-related components: 1) quantitative descriptions for 3D neuron morphologies, i.e., computing effective features that can differentiate subtle difference among massive neurons; 2) large-scale neuron mining, i.e., efficiently indexing neurons with similar morphologies in large-scale datasets; 3) interactively neuron exploration and visualization, i.e., developing neuron visualization tools and bring human in the loop to explore neurons in an interactive and immersive manner. We propose a series of methods in tackling problems related to the above three components. Regarding the quantitative description, we develop a deep learning framework based on neuron projection, which can transform 3D neurons into 2D images and learn effective neuron features. Regarding the large-scale neuron mining, binary coding methods are introduced, which can transform feature vectors into short binary codes for real-time indexing and mining. Regarding the interactive neuron exploration, we visualize 3D neurons using augmented reality (AR) techniques, where users can provide relevance feedback to further improve the mining performance. The proposed methods are validated on the currently largest neuron database including more than 58,000 neurons, achieving state-of-the-art performance in comparison with other related methods. More importantly, we demonstrate use cases of our framework in multiple neuron analysis and exploration tasks, showing its potential benefits in facilitating the research of neuroscience.
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