肝细胞癌
人工智能
计算机科学
相关性
序列(生物学)
采样(信号处理)
图像(数学)
金标准(测试)
放射科
深度学习
集合(抽象数据类型)
医学
模式识别(心理学)
内科学
数学
计算机视觉
几何学
滤波器(信号处理)
生物
遗传学
程序设计语言
作者
Chen Zhao,Tian Bai,Tongjia Chu,Wei Feng,Fa Zhang
标识
DOI:10.1109/bibm55620.2022.9994992
摘要
Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.
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