Online domain adaptation for continuous cross-subject liver viability evaluation based on irregular thermal data

Abstract

Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking an invasive biopsy of the liver. Recently, people have started to investigate the non-invasive evaluation of liver viability during its procurement using 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 article, 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 the classifier. Our proposed framework is applied to the liver procurement data, and classifies the liver viability accurately.

Keywords:

• Graph signal processing
• irregular data
• liver viability
• online domain adaptation
• real-time classification

Acknowledgments

The authors thank Dr. Ran Jin for sharing the data and Mr. Aditya Maunakbhai Patel for helping in data pre-processing.

Additional information

Notes on contributors

Sahand Hajifar

Sahand Hajifar is pursuing his PhD in the Department of Industrial and Systems Engineering at University at Buffalo. He received a bachelor’s degree in industrial engineering from the Iran University of Science and Technology in 2015 and a master’s degree in industrial engineering from Sharif University of Technology in 2017. His research interests include data analytics, signal processing, statistical quality control and their applications in manufacturing, ergonomics and healthcare.

Hongyue Sun

Hongyue Sun is an assistant professor with the Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA. He received a BE degree in mechanical engineering from the Beijing Institute of Technology, Beijing, China, in 2012, an MS degree in statistics, and a PhD degree in industrial engineering from Virginia Tech, Blacksburg, VA, USA, in 2015 and 2017, respectively. His research interests are data analytics for advanced manufacturing and healthcare systems. He is a member of INFOMRS, IISE, IEEE and ASME.