Scholar4AI Updated 2026-11-02 12:20 UTC+09:00

MICCAI: Medical Image Computing and Computer-Assisted Intervention — papers and institutions, 2024–2026

Papers of MICCAI, Medical Image Computing and Computer-Assisted Intervention (CORE rank A; field: Applied computing, Computer vision and multimedia computation), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. Also as Markdown.

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Key findings#

  • MICCAI (Medical Image Computing and Computer-Assisted Intervention) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 2,970 of its papers from 2024–2026 (862 in 2024, 943 in 2025, 1,165 in 2026).
  • By the countries of the authors' institutions, the most papers come from China (1,466), the United States (637) and the United Kingdom (364).
  • The institutions with the most papers are Shanghai Jiao Tong University (133), Chinese Academy of Sciences (124) and Chinese University of Hong Kong (106).
  • Its most-cited paper of these years is “SegMamba: Long-Range Sequential Modeling Mamba for 3D Medical Image Segmentation” (2024), with 607 citations.

Papers per year#

MICCAI papers per publication year in OpenAlex (–: not in OpenAlex yet)
YearPapersWith affiliations
2024862854
2025943939
20261,1651,159

Sources#

Where OpenAlex lists the papers
OpenAlex sourceWhich papers
S106296714Papers of the volumes titled “Medical Image Computing and Computer Assisted Intervention – MICCAI <year>” (LNCS), found by DOI through Crossref

Who publishes there#

Institutions with the most MICCAI papers, 2024–2026
#InstitutionPapers
1Shanghai Jiao Tong University133
2Chinese Academy of Sciences124
3Chinese University of Hong Kong106
4Technical University of Munich102
5Imperial College London93
6ShanghaiTech University90
7Harvard University88
8Hong Kong University of Science and Technology85
9Fudan University85
10Zhejiang University82
Countries with the most MICCAI papers, 2024–2026
#CountryPapers
1China1,466
2United States637
3United Kingdom364
4Hong Kong291
5Germany257
6South Korea196
7Canada127
8Australia119
9France110
10Singapore105
Keywords with the most MICCAI papers, 2024–2026
#KeywordPapers
1medical image segmentation239
2diffusion models127
3vision-language models125
4self-supervised learning101
5Segment Anything Model95
6contrastive learning94
7whole slide images94
8interpretability87
9medical image classification84
10multimodal fusion83

Most-cited papers#

Most-cited MICCAI papers of 2024–2026 (citations counted by OpenAlex)
#CitationsTitlePublishedAuthors
1607SegMamba: Long-Range Sequential Modeling Mamba for 3D Medical Image Segmentation2024-01-01Zhaohu Xing, Ye Tian, Yijun Yang et al.
2409nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation2024-01-01Fabian Isensee, Tassilo Wald, Constantin Ulrich et al.
3384Swin-UMamba: Mamba-Based UNet with ImageNet-Based Pretraining2024-01-01Jiarun Liu, Hao Yang, Hong-Yu Zhou et al.
4137LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation2024-01-01Jinhong Wang, Jintai Chen, Danny Z. Chen et al.
5114MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology2024-01-01Shu Yang, Yihui Wang, Hao Chen
699Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting2024-01-01Xian Lin, Yangyang Xiang, Li Yu et al.
791DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation2024-01-01Yifan Gao, Wei Xia, Dingdu Hu et al.
882BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection2024-01-01Ming Kang, Chee‐Ming Ting, Fung Fung Ting et al.
976Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models2024-01-01Nicholas Konz, Yuwen Chen, Haoyu Dong et al.
1067Endora: Video Generation Models as Endoscopy Simulators2024-01-01Chenxin Li, Hengyu Liu, Yifan Liu et al.

Conferences in the same field#

AAAI · CVPR · ECCV · IJCAI · ACMMM · ICCV · SIGGRAPH Asia · SIGGRAPH · WACV · ICME · ECAI · AIED · ICWSM · SIGSPATIAL · LAK · ISLPED · ISMB · MMSys

Cite this page

Scholar4AI. (2026). MICCAI: Medical Image Computing and Computer-Assisted Intervention — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/miccai

@misc{scholar4ai-proceedings-miccai,
  author = {{Scholar4AI}},
  title = {MICCAI: Medical Image Computing and Computer-Assisted Intervention — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/miccai}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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