國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Efficient Transfer Learning Using Pre-Trained Models on CT/MRI./
作者:	Guobadia, Nicole.
面頁冊數:	1 online resource (9 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Medical imaging. -
電子資源:	click for full text (PQDT)
ISBN:	9798380326940

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

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

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

Thesis (M.S.)--University of Washington, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380326940Subjects--Topical Terms:

1180167
Medical imaging.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

554714
Electronic books.

Efficient Transfer Learning Using Pre-Trained Models on CT/MRI.
LDR:02628ntm a22003857 4500 001 1144274
005 20240531083816.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380326940
035 $a (MiAaPQ)AAI30527876
035 $a AAI30527876
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Guobadia, Nicole. $3 1469249
245 1 0 $a Efficient Transfer Learning Using Pre-Trained Models on CT/MRI.
264 0 $c 2023
300 $a 1 online 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 Advisor: Hu, Juhua.
502 $a Thesis (M.S.)--University of Washington, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Medical imaging. $3 1180167
650 4 $a Computer science. $3 573171
653 $a Computer vision
653 $a CT scans
653 $a Feature extraction
653 $a Fine-tuning
653 $a MRI
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0574
710 2 $a University of Washington. $b Computer Science and Systems. $3 1469250
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 85-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30527876 $z click for full text (PQDT)