期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 25, 期 1, 页码 143-151出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.2983126
关键词
Cancer; Feature extraction; Data integration; Biomedical imaging; Informatics; UGI cancer screening; clinical decision-making; multimodal data fusion
类别
资金
- National Natural Science Foundation of China [91846107]
- University Synergy Innovation Program of Anhui Province [GXXT-2019-044, GXXT-2019-014]
- Fundamental Research Funds for the Central Universities [PA2019GDQT0021, PA2019GDZC0100]
UGI cancer screening is critical in improving patient survival rates. The proposed SCNET framework utilizes semantic-level multimodal data fusion for UGI cancer screening, achieving an improvement of 4.01 on average.
Upper gastrointestinal (UGI) cancer has been identified as one of the ten most common causes of cancer deaths globally. UGI cancer screening is critical to improving the survival rate of UGI cancer patients. While many approaches to UGI cancer screening rely on single-modality data such as gastroscope imaging, limited studies have been dedicated to UGI cancer screening exploiting multisource and multimodal medical data, which could potentially lead to improved screening results. In this paper, we propose semantic-level cancer-screening network (SCNET), a framework for UGI cancer screening based on semantic-level multimodal upper gastrointestinal data fusion. Specifically, the proposed SCNET consists of a gastrointestinal image recognition flow and a textual medical record processing flow. High-level features of upper gastrointestinal data are extracted by identifying effective feature channels according to the correlation between the textual features and the spatial structure of the image features. The final screening results are obtained after the data fusion step. The experimental results show that the improvement of our approach over the state-of-the-art ones reached 4.01 in average. The source code of SCNET is available at https://github.com/netflymachine/SCNET.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据