TY - JOUR
T1 - A unified end-to-end classification model for focal liver lesions
AU - Zhao, Ling
AU - Liu, Shuaiqi
AU - An, Yanling
AU - Cai, Wenjia
AU - Li, Bing
AU - Wang, Shui Hua
AU - Liang, Ping
AU - Yu, Jie
AU - Zhao, Jie
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/9
Y1 - 2023/9
N2 - Accurate diagnosis of focal liver lesions (FLLs) plays a crucial role in patients’ management, surveillance, and prognosis. Contrast-enhanced ultrasound (CEUS) as a vital diagnostic tool for FLLs still faces the challenge of image feature overlap among several FLLs. In this study, we proposed a deep learning-based model, denoted as a unified end-to-end (UEE) model, to fully capture the lesion information to achieve the classification of FLLs by adopting CEUS. We first exploited ResNet50 as the backbone to extract multi-scale features from several CEUS frames. Secondly, the hybrid attention enhancement module (HAEM) was designed to enhance the significant features with various scales. The enhanced features were then concatenated and passed into the nested feature aggregation module (NFAM) to add nonlinearity to the features with various scales. Finally, all features from different frames were averaged and fed into a Sigmoid classifier for FLL classification. The experiments are developed on a multi-center dataset which ensured diversity. The extensive experimental results revealed that the UEE model achieved 88.64 % accuracy on benign (Be) and malignant (Ma) classification, and 91.27 % accuracy on hepatocellular carcinoma (HCC) and intrahepatic cholangiocellular carcinoma (ICC) classification.
AB - Accurate diagnosis of focal liver lesions (FLLs) plays a crucial role in patients’ management, surveillance, and prognosis. Contrast-enhanced ultrasound (CEUS) as a vital diagnostic tool for FLLs still faces the challenge of image feature overlap among several FLLs. In this study, we proposed a deep learning-based model, denoted as a unified end-to-end (UEE) model, to fully capture the lesion information to achieve the classification of FLLs by adopting CEUS. We first exploited ResNet50 as the backbone to extract multi-scale features from several CEUS frames. Secondly, the hybrid attention enhancement module (HAEM) was designed to enhance the significant features with various scales. The enhanced features were then concatenated and passed into the nested feature aggregation module (NFAM) to add nonlinearity to the features with various scales. Finally, all features from different frames were averaged and fed into a Sigmoid classifier for FLL classification. The experiments are developed on a multi-center dataset which ensured diversity. The extensive experimental results revealed that the UEE model achieved 88.64 % accuracy on benign (Be) and malignant (Ma) classification, and 91.27 % accuracy on hepatocellular carcinoma (HCC) and intrahepatic cholangiocellular carcinoma (ICC) classification.
KW - Deep learning
KW - Focal liver lesions
KW - Medical image classification Contrast-enhanced ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85165117875&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2023.105260
DO - 10.1016/j.bspc.2023.105260
M3 - Article
AN - SCOPUS:85165117875
SN - 1746-8094
VL - 86
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 105260
ER -