國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Lightweight Deep Learning for Biomed...

University of California, San Diego.

Lightweight Deep Learning for Biomedical Image Segmentation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Lightweight Deep Learning for Biomedical Image Segmentation.
作者:	Uys, Dylan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:	67 p.
附註:	Source: Masters Abstracts International, Volume: 81-04.
附註:	Advisor: Cottrell, Garrison W.
Contained By:	Masters Abstracts International81-04.
標題:	Artificial intelligence.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13887302
ISBN:	9781085650076

Lightweight Deep Learning for Biomedical Image Segmentation.
Uys, Dylan.

Lightweight Deep Learning for Biomedical Image Segmentation. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 67 p.

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.S.)--University of California, San Diego, 2019.

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

Many techniques for analyzing cardiovascular health rely on cardiac magnetic resonance images that have been segmented to identify various components of the heart. Manually segmenting these images is cumbersome and prone to variability, which calls for the development of accessible automation tools for cardiac researchers. In order to benefit the developing symbiosis between machine learning and medicine, such tools must be accurate, efficient and inferentially transparent. This paper introduces a U-Net-based pipeline for left ventricular segmentation of short-axis CMRs. The U-Net, a Fully Convolutional Network known for its success in biomedical image segmentation, is a natural candidate for our task. This work constitutes the core of a larger-scale project focused on improving human disease models through the acceleration of animal cardiac research. Accordingly, experiments discussed here leverage both human and animal data to explore the efficacy of image processing and model training strategies.This paper focuses on optimizing our U-Nets for resource constrained environments, and demonstrates that these models require only a fraction of their typical convolutional filters. This reduction affords efficiency with the added benefit of explainability by improving the practicality of visualizing learned features. Inference can also be further optimized by pruning trained models without any loss of accuracy or the need for retraining. Specifically, we show that U-Nets with less than 2% of their original parameters train in minutes on a single GPU and achieve Dice scores above 0.95 on multiple CMR datasets. Furthermore, inference on hundreds of images can be performed in seconds on a laptop.

ISBN: 9781085650076Subjects--Topical Terms:

194058
Artificial intelligence.

Lightweight Deep Learning for Biomedical Image Segmentation.
LDR:02689nmm a2200313 4500 001 570782
005 20200514111958.5
008 200901s2019 ||||||||||||||||| ||eng d
020 $a 9781085650076
035 $a (MiAaPQ)AAI13887302
035 $a AAI13887302
040 $a MiAaPQ $c MiAaPQ
100 1 $a Uys, Dylan. $3 857482
245 1 0 $a Lightweight Deep Learning for Biomedical Image Segmentation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 67 p.
500 $a Source: Masters Abstracts International, Volume: 81-04.
500 $a Advisor: Cottrell, Garrison W.
502 $a Thesis (M.S.)--University of California, San Diego, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Many techniques for analyzing cardiovascular health rely on cardiac magnetic resonance images that have been segmented to identify various components of the heart. Manually segmenting these images is cumbersome and prone to variability, which calls for the development of accessible automation tools for cardiac researchers. In order to benefit the developing symbiosis between machine learning and medicine, such tools must be accurate, efficient and inferentially transparent. This paper introduces a U-Net-based pipeline for left ventricular segmentation of short-axis CMRs. The U-Net, a Fully Convolutional Network known for its success in biomedical image segmentation, is a natural candidate for our task. This work constitutes the core of a larger-scale project focused on improving human disease models through the acceleration of animal cardiac research. Accordingly, experiments discussed here leverage both human and animal data to explore the efficacy of image processing and model training strategies.This paper focuses on optimizing our U-Nets for resource constrained environments, and demonstrates that these models require only a fraction of their typical convolutional filters. This reduction affords efficiency with the added benefit of explainability by improving the practicality of visualizing learned features. Inference can also be further optimized by pruning trained models without any loss of accuracy or the need for retraining. Specifically, we show that U-Nets with less than 2% of their original parameters train in minutes on a single GPU and achieve Dice scores above 0.95 on multiple CMR datasets. Furthermore, inference on hundreds of images can be performed in seconds on a laptop.
590 $a School code: 0033.
650 4 $a Artificial intelligence. $3 194058
650 4 $a Computer science. $3 199325
650 4 $a Medical imaging. $3 766168
690 $a 0800
690 $a 0984
690 $a 0574
710 2 $a University of California, San Diego. $b Computer Science and Engineering. $3 857483
773 0 $t Masters Abstracts International $g 81-04.
790 $a 0033
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13887302