TY - GEN
T1 - Hybrid Vision Transformer for Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy Videos
AU - Angelina, Clara Lavita
AU - Kai Chou, Yi
AU - Lee, Tsung Chun
AU - Kongkam, Pradermchai
AU - Han, Ming Lun
AU - Wang, Hsiu Po
AU - Chang, Hsuan Ting
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The early detection of pancreatic cystic lesions plays a significant role in the survival chance of a patient with pancreatic cancer. Yet it is still a huge challenge. Unfortunately, most pancreatic cancers were diagnosed when the tumor was metastatic. In this study, the Hybrid Transformer, which is the combination of the VGG19 network and Vision Transformer, is utilized as a learning model to predict the pancreatic cystic symptom types in needle-based confocal laser endomicroscopy. A total of 16,944 images containing five types of pancreatic cystic are collected as the training and validation data. Our method can automatically classify the feature type of pancreatic cystic in the test videos and record the prediction results frame by frame. In our experiment, the proposed method successfully identifies the symptom types of 13 from 18 test videos and achieves an accuracy as high as 72%.
AB - The early detection of pancreatic cystic lesions plays a significant role in the survival chance of a patient with pancreatic cancer. Yet it is still a huge challenge. Unfortunately, most pancreatic cancers were diagnosed when the tumor was metastatic. In this study, the Hybrid Transformer, which is the combination of the VGG19 network and Vision Transformer, is utilized as a learning model to predict the pancreatic cystic symptom types in needle-based confocal laser endomicroscopy. A total of 16,944 images containing five types of pancreatic cystic are collected as the training and validation data. Our method can automatically classify the feature type of pancreatic cystic in the test videos and record the prediction results frame by frame. In our experiment, the proposed method successfully identifies the symptom types of 13 from 18 test videos and achieves an accuracy as high as 72%.
KW - deep learning
KW - needle-based confocal laser endomicroscopy
KW - pancreatic cystic symptom
KW - VGG19
KW - vision transformer
UR - http://www.scopus.com/inward/record.url?scp=85174958487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174958487&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226747
DO - 10.1109/ICCE-Taiwan58799.2023.10226747
M3 - Conference contribution
AN - SCOPUS:85174958487
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 47
EP - 48
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -
