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Deep Learning for Automated Medical ...

Zhu, Wentao.

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Deep Learning for Automated Medical Image Analysis = = 基于深度学习的医疗图像自动分析.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Automated Medical Image Analysis =/
Reminder of title:	基于深度学习的医疗图像自动分析.
Author:	Zhu, Wentao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	120 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Contained By:	Dissertations Abstracts International81-02B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13807548
ISBN:	9781085582230

Deep Learning for Automated Medical Image Analysis = = 基于深度学习的医疗图像自动分析.
Zhu, Wentao.

Deep Learning for Automated Medical Image Analysis =基于深度学习的医疗图像自动分析. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 120 p.

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2019.

This item must not be sold to any third party vendors.

Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical specialists. The current practice of reading medical images is labor-intensive, time-consuming, costly, and error-prone. It would be more desirable to have a computer-aided system that can automatically make diagnosis and treatment recommendations.Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images. Early detection has proven to be critical to give patients the best chance of recovery and survival. Advanced computer-aided diagnosis systems are expected to have high sensitivities and small low positive rates. How to provide accurate diagnosis results and explore different types of clinical data is an important topic in the current computer-aided diagnosis research.In this thesis, we will introduce 1) mammograms for detecting breast cancers, the most frequently diagnosed solid cancer for U.S. women, 2) lung Computed Tomography (CT) images for detecting lung cancers, the most frequently diagnosed malignant cancer, and 3) head and neck CT images for automated delineation of organs at risk in radiotherapy. First, we will show how to employ the adversarial concept to generate the hard examples improving mammogram mass segmentation. Second, we will demonstrate how to use the weakly labelled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning. Third, the thesis will walk through DeepLung system which combines deep 3D ConvNets and Gradient Boosting Machine (GBM) for automated lung nodule detection and classification. Fourth, we will show how to use weakly labelled data to improve existing lung nodule detection system by integrating deep learning with probabilistic graphic model. Lastly, we will demonstrate the AnatomyNet which is thousands' faster and more accurate than previous methods on automated anatomy segmentation.

ISBN: 9781085582230Subjects--Topical Terms:

523869
Computer science.

Deep Learning for Automated Medical Image Analysis = = 基于深度学习的医疗图像自动分析.
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