论文标题
基于不规则热数据的连续跨主题肝活力评估的在线域适应
Online Domain Adaptation for Continuous Cross-Subject Liver Viability Evaluation Based on Irregular Thermal Data
论文作者
论文摘要
对肝脏在采购过程中的准确评估是一个具有挑战性的问题，传统上是通过对肝脏进行侵入性活检来解决的。最近，人们开始使用肝表面热图像对肝脏生存能力进行非侵入性评估。但是，现有的作品包括热图像中的背景噪声，并且不考虑肝脏的横向异质性，因此可以影响生存能力的精度。在本文中，我们建议使用纯肝区域的不规则热数据，以及跨受试者的肝脏评估信息（即，横向受试者肝脏中可用的生存能力标签信息），以实时评估新肝脏的生存能力。为了实现这一目标，我们根据图形信号处理（GSP）的工具提取不规则热数据的功能，并使用交叉对象肝的GSP功能提出了在线域适应（DA）和分类框架。基于MulticonVex块坐标坐标算法旨在在线DA期间共同学习域不变特征并学习分类器。我们提出的框架应用于肝脏采购数据，并准确地对肝脏生存能力进行了分类。
Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking invasive biopsy on liver. Recently, people have started to investigate on the non-invasive evaluation of liver viability during its procurement using the liver surface thermal images. However, existing works include the background noise in the thermal images and do not consider the cross-subject heterogeneity of livers, thus the viability evaluation accuracy can be affected. In this paper, we propose to use the irregular thermal data of the pure liver region, and the cross-subject liver evaluation information (i.e., the available viability label information in cross-subject livers), for the real-time evaluation of a new liver's viability. To achieve this objective, we extract features of irregular thermal data based on tools from graph signal processing (GSP), and propose an online domain adaptation (DA) and classification framework using the GSP features of cross-subject livers. A multiconvex block coordinate descent based algorithm is designed to jointly learn the domain-invariant features during online DA and learn the classifier. Our proposed framework is applied to the liver procurement data, and classifies the liver viability accurately.
