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| Presentation | 2021-03-04 16:20 VQA for Medical Image Data based on Image Feature Extraction and Fusion Hideo Umada, Masaki Aono (TUT)  PRMU2020-81 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, there has been a remarkable growth in research on deep learning in the fields of computer vision and natural language processing, and there are growing expectations about the application of artificial intelligence in various fields. As a result, there is a growing demand for research on the VQA-Med task, which is an application of Visual QA, a research that requires both computer vision and natural language processing techniques, to the medical field. Medical images include images from various modalities such as X-ray images, MRI images, and CT images. In this study, we consider QA problems as classification problems and propose a method for obtaining effective features for a variety of medical images and a feature synthesis method, Feature Fusion Networks, for learning multimodal relationships between images and questions. Using the VQA-Med2020 dataset, we experimented with and evaluated the system, and reported on the new findings. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Learning / Visual Question-Answering / Medical Image / Feature Extraction / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 409, PRMU2020-81, pp. 71-76, March 2021. | |
| Paper # | PRMU2020-81 | |
| Date of Issue | 2021-02-25 (PRMU) | |
| 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) | |
| Download PDF | PRMU2020-81 |
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| Conference Information | |
| Committee | PRMU IPSJ-CVIM |
| Conference Date | 2021-03-04 - 2021-03-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Computer Vision and Pattern Recognition for specific environment |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-03-PRMU-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | VQA for Medical Image Data based on Image Feature Extraction and Fusion |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Visual Question-Answering |
| Keyword(3) | Medical Image |
| Keyword(4) | Feature Extraction |
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| 1st Author's Name | Hideo Umada |
| 1st Author's Affiliation | Toyohashi University of Technology (TUT) |
| 2nd Author's Name | Masaki Aono |
| 2nd Author's Affiliation | Toyohashi University of Technology (TUT) |
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| Speaker | Author-1 |
| Date Time | 2021-03-04 16:20:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2020-81 |
| Volume (vol) | vol.120 |
| Number (no) | no.409 |
| Page | pp.71-76 |
| #Pages | 6 |
| Date of Issue | 2021-02-25 (PRMU) |
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