期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 27, 期 1, 页码 386-396出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2022.3220788
关键词
Feature extraction; Lesions; Liver; Correlation; Computed tomography; Medical diagnostic imaging; Image segmentation; Cross-lesion correlation; information fusion; liver lesions; multi-phase
In this study, a Knowledge-guided framework named MCCNet is proposed to adaptively integrate multi-phase liver lesion information and construct a lesion classification network. The effectiveness of the proposed modules in exploiting and fusing multi-phase information is demonstrated through extensive experimental results and evaluations on a dataset containing 3,683 lesions from 2,333 patients in 9 hospitals.
Automatic and accurate differentiation of liver lesions from multi-phase computed tomography imaging is critical for the early detection of liver cancer. Multi-phase data can provide more diagnostic information than single-phase data, and the effective use of multi-phase data can significantly improve diagnostic accuracy. Current fusion methods usually fuse multi-phase information at the image level or feature level, ignoring the specificity of each modality, therefore, the information integration capacity is always limited. In this paper, we propose a Knowledge-guided framework, named MCCNet, which adaptively integrates multi-phase liver lesion information from three different stages to fully utilize and fuse multi-phase liver information. Specifically, 1) a multi-phase self-attention module was designed to adaptively combine and integrate complementary information from three phases using multi-level phase features; 2) a cross-feature interaction module was proposed to further integrate multi-phase fine-grained features from a global perspective; 3) a cross-lesion correlation module was proposed for the first time to imitate the clinical diagnosis process by exploiting inter-lesion correlation in the same patient. By integrating the above three modules into a 3D backbone, we constructed a lesion classification network. The proposed lesion classification network was validated on an in-house dataset containing 3,683 lesions from 2,333 patients in 9 hospitals. Extensive experimental results and evaluations on real-world clinical applications demonstrate the effectiveness of the proposed modules in exploiting and fusing multi-phase information.
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