Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning.
10.7507/1001-5515.202208045
- Author:
Han WEN
1
;
Ying ZHAO
2
;
Yong YANG
1
;
Hongkai WANG
3
;
Ailian LIU
2
;
Yu YAO
1
;
Zhongliang FU
1
Author Information
1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610213, P. R. China.
2. The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P. R. China.
3. School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.
- Publication Type:Journal Article
- Keywords:
Classification;
Deep learning;
Hepatocellular carcinoma;
Multi-task learning;
Segmentation
- MeSH:
Humans;
Carcinoma, Hepatocellular;
Liver Neoplasms;
Learning
- From:
Journal of Biomedical Engineering
2023;40(1):60-69
- CountryChina
- Language:Chinese
-
Abstract:
Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.
