Relationship Between Training Data Size, Generalization Performance, and Saliency Maps in Regression Convolutional Neural Networks for Estimating Pulmonary Artery Wedge Pressure

Authors

Takuya Miura (ORCID: https://orcid.org/0000-0002-8991-5412)
Jun Toyotani
Yohei Kakimoto (ORCID: https://orcid.org/0000-0002-0346-3831)
Yasuo Okumura (ORCID: https://orcid.org/0000-0002-2960-4241)
Masaya Mori
Yuki Saito (ORCID: https://orcid.org/0000-0002-8193-9575)
Yuto Omae (ORCID: https://orcid.org/0000-0002-5924-6959)

Institutions

Nihon University (JP)
College of Industrial Technology (JP)

Publication Details

Journal: Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
Published: 2025-11-14
DOI: https://doi.org/10.3156/jsoft.37.4_708
Primary Topic: ECG Monitoring and Analysis
Type: article
Field-Weighted Citation Impact: 0.00

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article

Relationship Between Training Data Size, Generalization Performance, and Saliency Maps in Regression Convolutional Neural Networks for Estimating Pulmonary Artery Wedge Pressure

Takuya Miura, Jun Toyotani, Yohei Kakimoto, Yasuo Okumura et al.

Nov 14, 2025

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics

ECG Monitoring and Analysis

article

Relationship Between Training Data Size, Generalization Performance, and Saliency Maps in Regression Convolutional Neural Networks for Estimating Pulmonary Artery Wedge Pressure

Takuya Miura, Jun Toyotani, Yohei Kakimoto, Yasuo Okumura, Masaya Mori, Yuki Saito, Yuto Omae

November 14, 2025article en

Abstract

No abstract available for this paper.

Journal of Japan Society for Fuzzy Theory and Intelligent InformaticsVol. 37(4)

Nihon University (JP), College of Industrial Technology (JP)

Openalex Percentile: Top 48%

ECG Monitoring and Analysis

DOI Open Access PDF OpenAlex

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