@inproceedings{4fc663770b944b2fbec7a473858b850d,
title = "A New TDA-based machine learning Classifier Framework for Predicting Hepatic Decompensation from MR Images",
abstract = "Machine-learning-based solutions need sufficient manually labeled training data to produce accurate predictions, which can hinder their performance for rare diseases with limited data. We show how to use a newly developed algebraic topology-based machine learning method that analyzes the visual pattern of the data to accurately predict hepatic decompensation in patients with Primary Sclerosing Cholangitis. The results demonstrate that the proposed methodology discriminates between Early Decompensation and Not Early groups. We found that the algebraic topology-based machinelearning approach allows us to make accurate predictions from small datasets such as predicting early and not early hepatic decompensation.",
keywords = "Hepatic Decompensation, Machine learning, Persistent Homology, Topological data Analysis",
author = "Yashbir Singh and William Jons and Eaton, {John E.} and Sobek, {Joseph D.} and Jaidip Jagtap and Conte, {Gian Marco} and Fuemmeler, {Eric G.} and Kuan Zhang and Yujia Wei and Garcia, {Diana Victoria Vera} and Erickson, {Bradley J.}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2607312",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Deserno, {Thomas M.} and Deserno, {Thomas M.} and Park, {Brian J.}",
booktitle = "Medical Imaging 2022",
}