Medical Image Segmentation via Sparse Coding Decoder
Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more co...
Uloženo v:
| Vydáno v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
06.04.2025
|
| Témata: | |
| ISSN: | 2379-190X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more complex features. However, these models still exhibit limitations in fine-grained local feature extraction and effectively suppressing irrelevant information. To address this issue, a convolution sparse vector coding-based decoder is proposed, namely the CAScaded multi-layer Convolutional Sparse vector Coding DEcoder (CASCSCDE), which suppresses noise by refining the feature representation to be more sparse and accurate through sparse coding and localized convolution, effectively minimizing less important, noisy components. To demonstrate the effectiveness and versatility of our CASCSCDE, we incorporate our decoder into both pure Transformer and hybrid CNN-Transformer models, such as SwinUNet and TransUNet. Our experiments demonstrate that integrating CASCSCDE into the models significantly enhances segmentation performance. The CASCSCDE opens new ways for constructing decoders based on convolutional sparse vector coding. |
|---|---|
| AbstractList | Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more complex features. However, these models still exhibit limitations in fine-grained local feature extraction and effectively suppressing irrelevant information. To address this issue, a convolution sparse vector coding-based decoder is proposed, namely the CAScaded multi-layer Convolutional Sparse vector Coding DEcoder (CASCSCDE), which suppresses noise by refining the feature representation to be more sparse and accurate through sparse coding and localized convolution, effectively minimizing less important, noisy components. To demonstrate the effectiveness and versatility of our CASCSCDE, we incorporate our decoder into both pure Transformer and hybrid CNN-Transformer models, such as SwinUNet and TransUNet. Our experiments demonstrate that integrating CASCSCDE into the models significantly enhances segmentation performance. The CASCSCDE opens new ways for constructing decoders based on convolutional sparse vector coding. |
| Author | Wu, Kaigui Zhu, Mingwei Zeng, Long Li, Zefang |
| Author_xml | – sequence: 1 givenname: Long surname: Zeng fullname: Zeng, Long email: longzeng@stu.cqu.edu.cn organization: Chongqing University,School of Computer Science,Chongqing,China – sequence: 2 givenname: Mingwei surname: Zhu fullname: Zhu, Mingwei email: mingweizhu@stu.cqu.edu.cn organization: Chongqing University,School of Energy and Power Engineering,Chongqing,China – sequence: 3 givenname: Kaigui surname: Wu fullname: Wu, Kaigui email: kaiguiwu@cqu.edu.cn organization: Chongqing University,School of Computer Science,Chongqing,China – sequence: 4 givenname: Zefang surname: Li fullname: Li, Zefang email: zefangli@cqu.edu.cn organization: Chongqing University Qianjiang Hospital,Chongqing,China |
| BookMark | eNo1z19LwzAUBfAoCm5z38CH-AFab3KTNnmU-m8w2aB78G2kyW2JrO1oi-C3t6A-nYcDh99Zsquu74ixewGpEGAfNsVjWe6VzTJIJUidCjDGygwu2Nrm1qAGzEyuxCVbSMxtIix83LDlOH4CwFyYBVPvFKJ3J75pXUO8pKalbnJT7Dv-FR0vz24YiRd9iF3Dn8j3gYZbdl2700jrv1yxw8vzoXhLtrvXGbVNosUp0cajrFQOBLOwRuVqb2si0JXCoEg7pY2tBRKArKrZSQo95WC8yiB4XLG739lIRMfzEFs3fB__T-IPYlpIJA |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/ICASSP49660.2025.10889260 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350368741 |
| EISSN | 2379-190X |
| EndPage | 5 |
| ExternalDocumentID | 10889260 |
| Genre | orig-research |
| GroupedDBID | 23M 6IE 6IF 6IH 6IK 6IL 6IM 6IN AAJGR AAWTH ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP IPLJI M43 OCL RIE RIL RIO RNS |
| ID | FETCH-LOGICAL-i93t-58c32b470e0660f34afc9fee05b43d4e5a4589f13e002bb741e43ce708c460dc3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Nov 19 08:27:13 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-58c32b470e0660f34afc9fee05b43d4e5a4589f13e002bb741e43ce708c460dc3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10889260 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-April-6 |
| PublicationDateYYYYMMDD | 2025-04-06 |
| PublicationDate_xml | – month: 04 year: 2025 text: 2025-April-6 day: 06 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) |
| PublicationTitleAbbrev | ICASSP |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0008748 |
| Score | 2.294698 |
| Snippet | Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Biomedical imaging Convolution Convolutional codes Decoding Feature extraction Image coding Image segmentation Medical image segmentation Noise Sparse Coding Transformer Transformers Vectors |
| Title | Medical Image Segmentation via Sparse Coding Decoder |
| URI | https://ieeexplore.ieee.org/document/10889260 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwMhEJ7Yxhi9-KrxHUy8bmUXWOBoqo29NE22h96aBWZND902ff1-YbutevDgjZAAGQjzzcB8MwDPTuRxirSIhIe7iLvURXmhRMR1bFmO1ChnqmITst9Xo5Ee1GT1iguDiFXwGbZDs_rLdzO7Dk9l_oYrpb0B3oCGlOmWrLVXu0pydQRPdRLNl17nNcsGPCSf9F5gItq7wb_KqFQo0j395_pn0Prm45HBHmnO4QDLCzj5kUrwEnj940J6U68hSIaf05pVVJLNJCfZ3HuwSDqzMAN5w0BlX7Rg2H0fdj6iuiJCNNFsFQllWWK4pOgNBVownhdWF4hUGM4cR5FzoXQRM_R6zhhvLCBnFiVVlqfUWXYFzXJW4jUQa1Hp1ArjnA6YrhNnHUuckTK3scEbaAX5x_NtzovxTvTbP_rv4DjschXTkt5Dc7VY4wMc2s1qslw8Vif1BSkxlF4 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4oGh8XXxjf1sTrYnfb7rZHgxKISEiWAzeybWcNBx5B4PfbLgvqwYO3pkmbTpvON9PONwPwaEUWxkjzQDi4C7iNbZDlUgRchYZlSLW0uig2kXQ6st9X3ZKsXnBhELEIPsOabxZ_-XZiFv6pzN1wKZUzwLdhR3Ae0RVda6N4ZcLlHjyUaTSfWvXnNO1yn37S-YGRqK2H_yqkUuBI4-ifKziG6jcjj3Q3WHMCWzg-hcMfyQTPgJd_LqQ1cjqCpPgxKnlFY7IcZiSdOh8WSX3iZyAv6Mnssyr0Gq-9ejMoayIEQ8XmgZCGRZonFJ2pQHPGs9yoHJEKzZnlKDIupMpDhk7Tae3MBeTMYEKl4TG1hp1DZTwZ4wUQY1Cq2AhtrfKoriJrLIusTpLMhBovoerlH0xXWS8Ga9Gv_ui_h_1m7709aLc6b9dw4He8iHCJb6Ayny3wFnbNcj78nN0Vp_YFE-uXpQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+...+IEEE+International+Conference+on+Acoustics%2C+Speech+and+Signal+Processing+%281998%29&rft.atitle=Medical+Image+Segmentation+via+Sparse+Coding+Decoder&rft.au=Zeng%2C+Long&rft.au=Zhu%2C+Mingwei&rft.au=Wu%2C+Kaigui&rft.au=Li%2C+Zefang&rft.date=2025-04-06&rft.pub=IEEE&rft.eissn=2379-190X&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FICASSP49660.2025.10889260&rft.externalDocID=10889260 |