---
title: "MICCAI (A): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/miccai
markdown_url: https://scholar4ai.com/proceedings/miccai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# 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.

All conferences: https://scholar4ai.com/proceedings.md

## 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)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 862 | 854 |
| 2025 | 943 | 939 |
| 2026 | 1,165 | 1,159 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S106296714](https://openalex.org/S106296714) | Papers 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*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 133 |
| 2 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 124 |
| 3 | Chinese University of Hong Kong | 106 |
| 4 | Technical University of Munich | 102 |
| 5 | Imperial College London | 93 |
| 6 | ShanghaiTech University | 90 |
| 7 | Harvard University | 88 |
| 8 | Hong Kong University of Science and Technology | 85 |
| 9 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 85 |
| 10 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 82 |

*Countries with the most MICCAI papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 1,466 |
| 2 | United States | 637 |
| 3 | United Kingdom | 364 |
| 4 | Hong Kong | 291 |
| 5 | Germany | 257 |
| 6 | South Korea | 196 |
| 7 | Canada | 127 |
| 8 | Australia | 119 |
| 9 | France | 110 |
| 10 | Singapore | 105 |

*Keywords with the most MICCAI papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | medical image segmentation | 239 |
| 2 | diffusion models | 127 |
| 3 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 125 |
| 4 | self-supervised learning | 101 |
| 5 | Segment Anything Model | 95 |
| 6 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 94 |
| 7 | whole slide images | 94 |
| 8 | interpretability | 87 |
| 9 | medical image classification | 84 |
| 10 | [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 83 |

## Most-cited papers

*Most-cited MICCAI papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 607 | [SegMamba: Long-Range Sequential Modeling Mamba for 3D Medical Image Segmentation](https://doi.org/10.1007/978-3-031-72111-3_54) | 2024-01-01 | Zhaohu Xing, Ye Tian, Yijun Yang et al. |
| 2 | 409 | [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://doi.org/10.1007/978-3-031-72114-4_47) | 2024-01-01 | Fabian Isensee, Tassilo Wald, Constantin Ulrich et al. |
| 3 | 384 | [Swin-UMamba: Mamba-Based UNet with ImageNet-Based Pretraining](https://doi.org/10.1007/978-3-031-72114-4_59) | 2024-01-01 | Jiarun Liu, Hao Yang, Hong-Yu Zhou et al. |
| 4 | 137 | [LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation](https://doi.org/10.1007/978-3-031-72111-3_34) | 2024-01-01 | Jinhong Wang, Jintai Chen, Danny Z. Chen et al. |
| 5 | 114 | [MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology](https://doi.org/10.1007/978-3-031-72083-3_28) | 2024-01-01 | Shu Yang, Yihui Wang, Hao Chen |
| 6 | 99 | [Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting](https://doi.org/10.1007/978-3-031-72111-3_3) | 2024-01-01 | Xian Lin, Yangyang Xiang, Li Yu et al. |
| 7 | 91 | [DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation](https://doi.org/10.1007/978-3-031-72390-2_48) | 2024-01-01 | Yifan Gao, Wei Xia, Dingdu Hu et al. |
| 8 | 82 | [BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection](https://doi.org/10.1007/978-3-031-72111-3_4) | 2024-01-01 | Ming Kang, Chee‐Ming Ting, Fung Fung Ting et al. |
| 9 | 76 | [Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models](https://doi.org/10.1007/978-3-031-72104-5_9) | 2024-01-01 | Nicholas Konz, Yuwen Chen, Haoyu Dong et al. |
| 10 | 67 | [Endora: Video Generation Models as Endoscopy Simulators](https://doi.org/10.1007/978-3-031-72089-5_22) | 2024-01-01 | Chenxin Li, Hengyu Liu, Yifan Liu et al. |

## 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).