Mutual Evidential Deep Learning for Semi-supervised Medical Image Segmentation
Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliabi...
Uložené v:
| Vydané v: | Proceedings (IEEE International Conference on Bioinformatics and Biomedicine) s. 2010 - 2017 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
03.12.2024
|
| Predmet: | |
| ISSN: | 2156-1133 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliability of pseudo-labels from different sources. In this paper, we propose a mutual evidential deep learning (MEDL) framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning from two perspectives. First, we introduce networks with different architectures to generate complementary evidence for unlabeled samples and adopt an improved class-aware evidential fusion to guide the confident synthesis of evidential predictions sourced from diverse architectural networks. Second, utilizing the uncertainty in the fused evidence, we design an asymptotic Fisher information-based evidential learning strategy. This strategy enables the model to initially focus on unlabeled samples with more reliable pseudo-labels, gradually shifting attention to samples with lower-quality pseudo-labels while avoiding over-penalization of mislabeled classes in high data uncertainty samples. Additionally, for labeled data, we continue to adopt an uncertainty-driven asymptotic learning strategy, gradually guiding the model to focus on challenging voxels. Extensive experiments on five mainstream datasets have demonstrated that MEDL achieves state-of-the-art performance. |
|---|---|
| AbstractList | Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliability of pseudo-labels from different sources. In this paper, we propose a mutual evidential deep learning (MEDL) framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning from two perspectives. First, we introduce networks with different architectures to generate complementary evidence for unlabeled samples and adopt an improved class-aware evidential fusion to guide the confident synthesis of evidential predictions sourced from diverse architectural networks. Second, utilizing the uncertainty in the fused evidence, we design an asymptotic Fisher information-based evidential learning strategy. This strategy enables the model to initially focus on unlabeled samples with more reliable pseudo-labels, gradually shifting attention to samples with lower-quality pseudo-labels while avoiding over-penalization of mislabeled classes in high data uncertainty samples. Additionally, for labeled data, we continue to adopt an uncertainty-driven asymptotic learning strategy, gradually guiding the model to focus on challenging voxels. Extensive experiments on five mainstream datasets have demonstrated that MEDL achieves state-of-the-art performance. |
| Author | He, Yuanpeng Li, Lijian Pun, Chi-Man Jiao, Wenpin Bi, Yali Jin, Zhi |
| Author_xml | – sequence: 1 givenname: Yuanpeng surname: He fullname: He, Yuanpeng email: heyuanpeng@stu.pku.edu.cn organization: Peking University,Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science,Beijng,China – sequence: 2 givenname: Yali surname: Bi fullname: Bi, Yali email: biyali812@outlook.com organization: Southwest University,College of Computer and Information Science School of Software,Chongqing,China – sequence: 3 givenname: Lijian surname: Li fullname: Li, Lijian email: mc35305@umac.mo organization: Science University of Macau,Department of Computer and Information,Macau,China – sequence: 4 givenname: Chi-Man surname: Pun fullname: Pun, Chi-Man email: cmpun@um.edu.mo organization: Science University of Macau,Department of Computer and Information,Macau,China – sequence: 5 givenname: Wenpin surname: Jiao fullname: Jiao, Wenpin email: jwp@pku.edu.cn organization: Peking University,Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science,Beijng,China – sequence: 6 givenname: Zhi surname: Jin fullname: Jin, Zhi email: zhijin@pku.edu.cn organization: Peking University,Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science,Beijng,China |
| BookMark | eNo1UNtKw0AUXEXBWvsHgvmB1HP2lt1HW6sGEn2w72XTPSkrTRpyKfj3LqhPMzAXhrllV-2pJcYeEJaIYB9X-arUXHC15MDlEsFwDmAu2MJm1ggFwmjO9SWbcVQ6RRTihi2G4QsAMCqIasbey2mc3DHZnIOndgyRPhN1SUGub0N7SOpTn3xSE9Jh6qg_h4F8UpIP--jMG3egqB6aGHVjOLV37Lp2x4EWfzhn25fNdv2WFh-v-fqpSANmekwFWe6MdxKkr6QAWSsLXBFRRiQM1pY0WfAg97LSWFVxq0dulamUEk7M2f1vbYiRXdeHxvXfu_8HxA99_1Gc |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/BIBM62325.2024.10822008 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 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 | Biology |
| EISBN | 9798350386226 |
| EISSN | 2156-1133 |
| EndPage | 2017 |
| ExternalDocumentID | 10822008 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 10.13039/501100001809 |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IN AAJGR ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i176t-3e92a8da404db4304f59025eee7ee381f9e6e90d04c4b61bb115d12958b553a3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001446153502017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Jan 22 08:32:21 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-3e92a8da404db4304f59025eee7ee381f9e6e90d04c4b61bb115d12958b553a3 |
| PageCount | 8 |
| ParticipantIDs | ieee_primary_10822008 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-Dec.-3 |
| PublicationDateYYYYMMDD | 2024-12-03 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-Dec.-3 day: 03 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (IEEE International Conference on Bioinformatics and Biomedicine) |
| PublicationTitleAbbrev | BIBM |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0001622115 |
| Score | 1.9353065 |
| Snippet | Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 2010 |
| SubjectTerms | Biomedical imaging Class-aware evidential fusion Data models Deep learning Heterogeneous networks Image segmentation Mutual evidential deep learning Predictive models Reliability Semi-supervised medical segmentation Semisupervised learning Training Uncertainty |
| Title | Mutual Evidential Deep Learning for Semi-supervised Medical Image Segmentation |
| URI | https://ieeexplore.ieee.org/document/10822008 |
| WOSCitedRecordID | wos001446153502017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEG2UaOLJL4z4lR68Lu5u2217RSVygJDIgRvZtgMhkYUAa-K_d9pdJB48eGt206SZyfR12nnzCHnkjFtwwCOmck_JcSJSFkcI_cZKlnuICWITcjBQ47Ee1mT1wIUBgFB8Bm0_DG_5bmlLf1WGEY5wFqi9h1JmFVlrf6GSpZjMiLqGK4n1U6fX6SO6pwLTwJS3d7N_6agEGOme_nMBZ6S5J-TR4Q_UnJMDKC7IcaUj-XVJBv3S00BoJRGKMftBXwBWtG6eOqN4MqXvsJhHm3LlN4cNOFo_0dDeArcU_Dtb1DSkoklG3dfR81tUCyVE80Rm24iBTnPlch5zZziL-dQ3ZRG4fAmAkDzVkIGOXcwtN1liDNrJoReEMkKwnF2RRrEs4JpQI9JMcs1Tayw6USvwSVOCpxwMVcVVizS9VSarqhXGZGeQmz--35ITb_tQ_8HuSGO7LuGeHNnP7XyzfggO_AYkB5n5 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG4MavTkLwz-7sHrcFvbrbuiEoiwkMiBG1nbByGRQYCZ-N_72g2JBw_emi1Nmvfy-vW173sfIY-ccQ0GuMdkZik5RnhS4wihX-mYZRZinNhEnKZyNEoGFVndcWEAwBWfQdMO3Vu-WejCXpVhhCOcOWrvvuA89Eu61u5KJQoxnRFVFVfgJ0-tbquP-B4KTARD3tzO_6Wk4oCkffLPJZyS-o6SRwc_YHNG9iA_J4elkuTXBUn7hSWC0FIkFKP2g74ALGnVPnVK8WxK32E-89bF0m4PazC0eqSh3TluKvh3Oq-ISHmdDNuvw-eOV0kleLMgjjYegyTMpMm4z43izOcT25ZF4PJjAATlSQIRJL7xueYqCpRCOxn0g5BKCJaxS1LLFzk0CFUijGKe8FArjW5MJNi0KcBzDgar5PKK1K1VxsuyGcZ4a5DrP74_kKPOsN8b97rp2w05tn5w1SDsltQ2qwLuyIH-3MzWq3vnzG9ZUJ1A |
| 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+%28IEEE+International+Conference+on+Bioinformatics+and+Biomedicine%29&rft.atitle=Mutual+Evidential+Deep+Learning+for+Semi-supervised+Medical+Image+Segmentation&rft.au=He%2C+Yuanpeng&rft.au=Bi%2C+Yali&rft.au=Li%2C+Lijian&rft.au=Pun%2C+Chi-Man&rft.date=2024-12-03&rft.pub=IEEE&rft.eissn=2156-1133&rft.spage=2010&rft.epage=2017&rft_id=info:doi/10.1109%2FBIBM62325.2024.10822008&rft.externalDocID=10822008 |