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| Paper Abstract and Keywords | ||
| Presentation | 2024-02-20 14:30 Optimizing Division Schemes with Mixture of Experts for Medical Data Compression Jiancheng Zhao, Takefumi Ogawa (Utokyo)  ITS2023-73 IE2023-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Implicit Neural Representation (INR) is an emerging technique for data compression that utilizes the parameters of a Deep Neural Network (DNN) to represent data. Current methods manually divide a complex scene into local regions and fit the INRs to these regions. This manual partitioning of a complex scene is a challenging task and does not allow for the simultaneous learning of the partition and INRs. To address this issue, we introduce MoEC, an innovative implicit neural compression method grounded in the mixture of experts theory. We employ a gating network to automatically allocate a specific INR to a 3D point in the scene. This gating network is trained in conjunction with the INRs of various local regions. Our learnable partition, in comparison to block-wise and tree-structured partitions, can adaptively identify the optimal partition in an end-to-end fashion. We perform comprehensive experiments on a large and diverse set of biomedical data to highlight the benefits of MoEC over existing methods. In the majority of our experimental setups, we have achieved leading-edge results. Notably, even at extreme compression ratios, such as 6000x, we maintain a PSNR of 48.16. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Medical data / Compression / INR / MoE / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 381, IE2023-62, pp. 145-150, Feb. 2024. | |
| Paper # | IE2023-62 | |
| Date of Issue | 2024-02-12 (ITS, IE) | |
| 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 | ITS2023-73 IE2023-62 |
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| Conference Information | |
| Committee | ITS IE ITE-MMS ITE-ME ITE-AIT |
| Conference Date | 2024-02-19 - 2024-02-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Processing, etc. |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2024-02-ITS-IE-MMS-ME-AIT |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Optimizing Division Schemes with Mixture of Experts for Medical Data Compression |
| Sub Title (in English) | |
| Keyword(1) | Medical data |
| Keyword(2) | Compression |
| Keyword(3) | INR |
| Keyword(4) | MoE |
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| 1st Author's Name | Jiancheng Zhao |
| 1st Author's Affiliation | The University of Tokyo (Utokyo) |
| 2nd Author's Name | Takefumi Ogawa |
| 2nd Author's Affiliation | The University of Tokyo (Utokyo) |
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| Speaker | Author-1 |
| Date Time | 2024-02-20 14:30:00 |
| Presentation Time | 15 minutes |
| Registration for | IE |
| Paper # | ITS2023-73, IE2023-62 |
| Volume (vol) | vol.123 |
| Number (no) | no.380(ITS), no.381(IE) |
| Page | pp.145-150 |
| #Pages | 6 |
| Date of Issue | 2024-02-12 (ITS, IE) |
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