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Deep Learning for Brain Tumor Segmen...

University of Colorado Colorado Springs.

Deep Learning for Brain Tumor Segmentation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep Learning for Brain Tumor Segmentation./
作者:	Moreno Lopez, Marc.
面頁冊數:	1 online resource (78 pages)
附註:	Source: Masters Abstracts International, Volume: 56-04.
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9781369727623

Deep Learning for Brain Tumor Segmentation.
Moreno Lopez, Marc.

Deep Learning for Brain Tumor Segmentation. - 1 online resource (78 pages)

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--University of Colorado Colorado Springs, 2017.

Includes bibliographical references

In this work, we present a novel method to segment brain tumors using deep learning. An accurate brain tumor segmentation is key for a patient to get the right treatment and for the doctor who must perform surgery. Due to the genetic differences that exist in different patients, even between the same kind of tumor, an accurate segmentation is crucial. To beat state-of-the-art methods, we want to use technology that has provided major breakthroughs in many different areas, including segmentation, deep learning, a new area of machine learning. It is a branch of machine learning that is attempting to model high level abstractions in data. We will be using Convolutional Neural Networks, CNNs, and we will evaluate the results that we obtain comparing our method against the best results obtained from the Brain Tumor Segmentation Challenge, BRATS.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369727623Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning for Brain Tumor Segmentation.
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100 1 $a Moreno Lopez, Marc. $3 1188611
245 1 0 $a Deep Learning for Brain Tumor Segmentation.
264 0 $c 2017
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336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: Jonathan Ventura.
502 $a Thesis (M.S.)--University of Colorado Colorado Springs, 2017.
504 $a Includes bibliographical references
520 $a In this work, we present a novel method to segment brain tumors using deep learning. An accurate brain tumor segmentation is key for a patient to get the right treatment and for the doctor who must perform surgery. Due to the genetic differences that exist in different patients, even between the same kind of tumor, an accurate segmentation is crucial. To beat state-of-the-art methods, we want to use technology that has provided major breakthroughs in many different areas, including segmentation, deep learning, a new area of machine learning. It is a branch of machine learning that is attempting to model high level abstractions in data. We will be using Convolutional Neural Networks, CNNs, and we will evaluate the results that we obtain comparing our method against the best results obtained from the Brain Tumor Segmentation Challenge, BRATS.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Colorado Colorado Springs. $b College of Engineering and Applied Science-Computer Science. $3 1182214
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10269643 $z click for full text (PQDT)

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