Cardiomyopathy diagnosis model from endomyocardial biopsy specimens: Appropriate feature space and class boundary in small sample size data

As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, endomyocardial biopsy specimens are often limited in sample size and require techniques such as feature extraction and di...

Full description

Bibliographic Details

Published in:	AIMS Bioengineering
Main Authors:	Masaya Mori, Yuto Omae, Yutaka Koyama, Kazuyuki Hara, Jun Toyotani, Yasuo Okumura, Hiroyuki Hao
Format:	Article
Language:	English
Published:	AIMS Press 2025-06-01
Subjects:	cardiomyopathy endomyocardial biopsy pathology image analysis machine learning texture analysis dimensionality reduction low sample size
Online Access:	https://www.aimspress.com/article/doi/10.3934/bioeng.2025014