Dataset for "Content-based image retrieval for the diagnosis of myocardial perfusion imaging using deep convolutional autoencoder"

Name: Dataset for "Content-based image retrieval for the diagnosis of myocardial perfusion imaging using deep convolutional autoencoder"
Creator: Akinori Higaki
Published: 2022-01-18T21:16:44.618Z
Keywords: Nuclear Medicine, Single-Photon Emission Computed Tomography, Machine Learning, Cardiology, Deep Learning

Higaki, Akinori; Kawaguchi, Naoto; Kazatani, Takuro; Okabe, Hikaru; Tanaka, Yuta; Kawada, Yoshitaka; Okayama, Hideki

doi:10.17632/mjhhw3zdwv.1

Dataset for "Content-based image retrieval for the diagnosis of myocardial perfusion imaging using deep convolutional autoencoder"

Published: 18 January 2022| Version 1 | DOI: 10.17632/mjhhw3zdwv.1

Contributors:

, Naoto Kawaguchi, Takuro Kazatani, Hikaru Okabe, Yuta Tanaka, Yoshitaka Kawada, Hideki Okayama

Description

Minimal dataset to reproduce our study results. Python codes used in the study are included. We showed that deep convolutional autoencoder can extract clinical information from myocardial perfusion imaging.

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Ehime Kenritsu Chuo Byoin

Dataset for "Content-based image retrieval for the diagnosis of myocardial perfusion imaging using deep convolutional autoencoder"

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