Multi Modal Deep Learning Based on Feature Attention for Prediction of Blood Clot Elasticity

Jiseon Moon, Sang-il Ahn, M. Joo, K. Park, H. Baac, Jitae Shin
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Abstract

Blood clot is formed inside a blood vessel with various reasons. Carotid artery is major blood vessel in the neck that supply blood to the brain. If blood clot is harden in carotid artery, blood clot of carotid artery can block the blood vessel and make narrowed blood vessel. Therefore, it is essential to predict the coagulation of blood clot in blood vessels. In this paper, we propose the method to determine the coagulation progress of blood clot. We use different two data which are waveform of blood clot and frequency spectra data obtained by applying the Fourier transform to the waveform data. And then feature vectors are extracted from two different data. We apply an encoder block network for waveform data and propose a feature attention network for frequency spectra data. The extracted feature vectors are classified into 3 stages of coagulation progress through multi-modal deep learning. Through the proposed method, we show a meaningful result with an accuracy of 98% in determining the stage of coagulation of blood clot.
基于特征关注的多模态深度学习预测血凝块弹性
血凝块是由于各种原因在血管内形成的。颈动脉是颈部的主要血管,为大脑供血。如果颈动脉内的血凝块变硬,颈动脉的血凝块会堵塞血管,使血管变窄。因此,预测血管中血块的凝固情况是十分必要的。本文提出了一种测定血凝块凝固过程的方法。我们使用两种不同的数据,即血凝块的波形数据和对波形数据进行傅里叶变换得到的频谱数据。然后从两个不同的数据中提取特征向量。对波形数据采用编码器块网络,对频谱数据提出特征关注网络。通过多模态深度学习将提取的特征向量划分为凝固过程的3个阶段。通过所提出的方法,我们在确定血凝块的凝固阶段方面取得了有意义的结果,准确率为98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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