東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Efficient Transfer Learning Using Pr...

Guobadia, Nicole,

FindBook

Google Book

Amazon

博客來

Efficient Transfer Learning Using Pre-Trained Models on CT/MRI /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficient Transfer Learning Using Pre-Trained Models on CT/MRI // Nicole Guobadia.
作者:	Guobadia, Nicole,
面頁冊數:	1 electronic resource (9 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30527876
ISBN:	9798380326940

Efficient Transfer Learning Using Pre-Trained Models on CT/MRI /
Guobadia, Nicole,

Efficient Transfer Learning Using Pre-Trained Models on CT/MRI /Nicole Guobadia. - 1 electronic resource (9 pages)

Source: Masters Abstracts International, Volume: 85-03.

The medical imaging field has unique obstacles to face when performing computer vision classification tasks. The retrieval of the data, be it CT scans or MRI, is not only expensive but also limited due to the lack of publicly available labeled data. In spite of this, clinicians often need this medical imaging data to perform diagnosis and recommendations for treatment. This motivates the use of efficient transfer learning techniques to not only condense the complexity of the data as it is often volumetric, but also to achieve better results faster through the use of established machine learning techniques like transfer learning, fine-tuning, and shallow deep learning. In this paper, we introduce a three-step process to perform classification using CT scans and MRI data. The process makes use of fine-tuning to align the pretrained model with the target class, feature extraction to preserve learned information for downstream classification tasks, and shallow deep learning to perform subsequent training. Experiments are done to compare the performance of the proposed methodology as well as the time cost trade offs for using our technique compared to other baseline methods. Through these experiments we find that our proposed method outperforms all other baselines while achieving a substantial speed up in overall training time.

English

ISBN: 9798380326940Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computer vision

Efficient Transfer Learning Using Pre-Trained Models on CT/MRI /
LDR:02655nmm a22004213i 4500 001 2396177
005 20250522083204.5
006 m o d
007 cr|nu||||||||
008 251215s2023 miu||||||m |||||||eng d
020 $a 9798380326940
035 $a (MiAaPQD)AAI30527876
035 $a AAI30527876
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Guobadia, Nicole, $e author. $3 3765788
245 1 0 $a Efficient Transfer Learning Using Pre-Trained Models on CT/MRI / $c Nicole Guobadia.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (9 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-03.
500 $a Advisors: Hu, Juhua Committee members: Kumar, Vikas.
502 $b M.S. $c University of Washington $d 2023.
520 $a The medical imaging field has unique obstacles to face when performing computer vision classification tasks. The retrieval of the data, be it CT scans or MRI, is not only expensive but also limited due to the lack of publicly available labeled data. In spite of this, clinicians often need this medical imaging data to perform diagnosis and recommendations for treatment. This motivates the use of efficient transfer learning techniques to not only condense the complexity of the data as it is often volumetric, but also to achieve better results faster through the use of established machine learning techniques like transfer learning, fine-tuning, and shallow deep learning. In this paper, we introduce a three-step process to perform classification using CT scans and MRI data. The process makes use of fine-tuning to align the pretrained model with the target class, feature extraction to preserve learned information for downstream classification tasks, and shallow deep learning to perform subsequent training. Experiments are done to compare the performance of the proposed methodology as well as the time cost trade offs for using our technique compared to other baseline methods. Through these experiments we find that our proposed method outperforms all other baselines while achieving a substantial speed up in overall training time.
546 $a English
590 $a School code: 0250
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
653 $a Computer vision
653 $a CT scans
653 $a Feature extraction
653 $a Fine-tuning
653 $a MRI
690 $a 0984
690 $a 0574
710 2 $a University of Washington. $b Computer Science and Systems. $e degree granting institution. $3 3765789
720 1 $a Hu, Juhua $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-03.
790 $a 0250
791 $a M.S.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30527876