Online Domain Adaptation for Continuous Cross-Subject Liver Viability Evaluation Based on Irregular Thermal Data

• URL: http://arxiv.org/abs/2011.12408v1
• Date: Tue, 24 Nov 2020 21:42:19 GMT
• Title: Online Domain Adaptation for Continuous Cross-Subject Liver Viability Evaluation Based on Irregular Thermal Data
• Authors: Sahand Hajifar and Hongyue Sun
• Abstract summary: We use irregular thermal data of pure liver region, and the cross-subject liver evaluation information for real-time evaluation of new liver's viability. Our proposed framework is applied to the liver procurement data, and the evaluation of the liver viability accurately.
• Score: 0.47267770920095525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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