| 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) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Emerging Implicit Neural Representation (INR) is a promising data compression technique, which represents the data using the parameters of a Deep Neural Network (DNN). Existing methods manually partition a complex scene into local regions and overfit the INRs into those regions. However, manually designing the partition scheme for a complex scene is very challenging and fails to jointly learn the partition and INRs. To solve the problem, we propose MoEC, a novel implicit neural compression method based on the theory of mixture of experts. Specifically, we use a gating network to automatically assign a specific INR to a 3D point in the scene. The gating network is trained jointly with the INRs of different local regions. Compared with block-wise and tree-structured partitions, our learnable partition can adaptively find the optimal partition in an end-to-end manner. We conduct detailed experiments on massive and diverse biomedical data to demonstrate the advantages of MoEC against existing approaches. In most of experiment settings, we have achieved state-of-the-art results. Especially in cases of extreme compression ratios, such as 6000x, we are able to uphold the PSNR of 48.16. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Medical data / Compression / INR / MoE / / / / |
| Reference Info. |
ITE Tech. Rep. |
| Paper # |
|
| Date of Issue |
|
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
|
| Conference Information |
| Committee |
IEICE-ITS IEICE-IE ME AIT MMS |
| 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 |
IEICE-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 |
| Keyword(5) |
|
| Keyword(6) |
|
| Keyword(7) |
|
| Keyword(8) |
|
| 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) |
| 3rd Author's Name |
|
| 3rd Author's Affiliation |
() |
| 4th Author's Name |
|
| 4th Author's Affiliation |
() |
| 5th Author's Name |
|
| 5th Author's Affiliation |
() |
| 6th Author's Name |
|
| 6th Author's Affiliation |
() |
| 7th Author's Name |
|
| 7th Author's Affiliation |
() |
| 8th Author's Name |
|
| 8th Author's Affiliation |
() |
| 9th Author's Name |
|
| 9th Author's Affiliation |
() |
| 10th Author's Name |
|
| 10th Author's Affiliation |
() |
| 11th Author's Name |
|
| 11th Author's Affiliation |
() |
| 12th Author's Name |
|
| 12th Author's Affiliation |
() |
| 13th Author's Name |
|
| 13th Author's Affiliation |
() |
| 14th Author's Name |
|
| 14th Author's Affiliation |
() |
| 15th Author's Name |
|
| 15th Author's Affiliation |
() |
| 16th Author's Name |
|
| 16th Author's Affiliation |
() |
| 17th Author's Name |
|
| 17th Author's Affiliation |
() |
| 18th Author's Name |
|
| 18th Author's Affiliation |
() |
| 19th Author's Name |
|
| 19th Author's Affiliation |
() |
| 20th Author's Name |
|
| 20th Author's Affiliation |
() |
| Speaker |
Author-1 |
| Date Time |
2024-02-20 14:30:00 |
| Presentation Time |
15 minutes |
| Registration for |
IEICE-IE |
| Paper # |
|
| Volume (vol) |
vol.48 |
| Number (no) |
|
| Page |
|
| #Pages |
|
| Date of Issue |
|