Paper Abstract and Keywords |
Presentation |
2020-01-29 14:00
Effects of skull and surrounding area in functional MRI analysis using Convolutional Neural Network Yuta Hosoi, Takafumi Hayashi (Niigata Univ.) MI2019-86 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A fuctional MRI(fMRI) has been used in various fields from medicine and neuroscience to psychology andlinguistics since its introduction in the 1990s. A fMRI has more spatial resolution among non-invasive brain measuringinstruments, and is said to be effective for identifying brain funtions and estimating mental disorders. Tools for analyzing brain activity obtained from fMRI have been developed by many researchers and companies to date, and statistical analysis and regression models were used in many analysis methods. In addition, with the advent of deep learning in recent years and the improvement of computer performance, analysis methods using machine learning are being actively studied. Among them, many studies have applied Convolutional Neural Network to fMRI images. In the future, in order to extract more information from fMRI images, a whole-brain analysis will be conducted to analyze the state of the brain receiving multiple is required. However, in deep learning that is strongly affected by noise, The skull and surrounding area may be affected by noise. In this paper, we report the effects of the skull and surrounding area using Grad-CAM, a feature visualization technique. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
functional MRI / CNN / Grad-CAM / skull / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 399, MI2019-86, pp. 91-96, Jan. 2020. |
Paper # |
MI2019-86 |
Date of Issue |
2020-01-22 (MI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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MI2019-86 |
Conference Information |
Committee |
MI |
Conference Date |
2020-01-29 - 2020-01-30 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
OKINAWAKEN SEINENKAIKAN |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Medical Image Engineering, Analysis, Recognition, etc. |
Paper Information |
Registration To |
MI |
Conference Code |
2020-01-MI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Effects of skull and surrounding area in functional MRI analysis using Convolutional Neural Network |
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functional MRI |
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CNN |
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Grad-CAM |
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skull |
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1st Author's Name |
Yuta Hosoi |
1st Author's Affiliation |
Niigata University (Niigata Univ.) |
2nd Author's Name |
Takafumi Hayashi |
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Niigata University (Niigata Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-01-29 14:00:00 |
Presentation Time |
10 minutes |
Registration for |
MI |
Paper # |
MI2019-86 |
Volume (vol) |
vol.119 |
Number (no) |
no.399 |
Page |
pp.91-96 |
#Pages |
6 |
Date of Issue |
2020-01-22 (MI) |
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