Online Domain Adaptation for Continuous Cross-Subject Liver Viability Evaluation Based on Irregular Thermal Data
This work provides an incremental improvement for medical professionals by offering a more accurate and non-invasive method for liver viability evaluation during procurement, potentially reducing the need for invasive biopsies.
This paper addresses the challenge of real-time, non-invasive liver viability evaluation during procurement by using irregular thermal data from the pure liver region and leveraging cross-subject liver evaluation information. The proposed online domain adaptation and classification framework, based on graph signal processing features, accurately classifies liver viability.
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.