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| Presentation | 2024-03-03 09:41 A preliminary study on deep causal discovery model for image classification Ryohei Motoda, Megumi Nakao (Kyoto Univ.)  MI2023-33 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Although saliency map used in image classification can visualize the regions correlated with predicted class, it cannot handle the existence and direction of causality. In this study, we propose a deep causal discovery model that aims to visualize the causality in the dataset used for image classification. In this model, the causal relationships between patches obtained by image segmentation are extracted by a graph generation model and visualized as a causal graph. We report the validation of the proposed model on MNIST and mandibular reconstruction planning data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Interpretability / causal discovery / image classification / Mandibular reconstruction / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 411, MI2023-33, pp. 11-14, March 2024. | |
| Paper # | MI2023-33 | |
| Date of Issue | 2024-02-25 (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) | |
| Download PDF | MI2023-33 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2024-03-03 - 2024-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | OKINAWAKEN SEINENKAIKAN |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Imaging, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2024-03-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A preliminary study on deep causal discovery model for image classification |
| Sub Title (in English) | |
| Keyword(1) | Interpretability |
| Keyword(2) | causal discovery |
| Keyword(3) | image classification |
| Keyword(4) | Mandibular reconstruction |
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| 1st Author's Name | Ryohei Motoda |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Megumi Nakao |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2024-03-03 09:41:00 |
| Presentation Time | 12 minutes |
| Registration for | MI |
| Paper # | MI2023-33 |
| Volume (vol) | vol.123 |
| Number (no) | no.411 |
| Page | pp.11-14 |
| #Pages | 4 |
| Date of Issue | 2024-02-25 (MI) |
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