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researcher

Jun Ma

8 papers hereh-index 114.9k citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author2

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • eess.IV6
  • cs.CV2
same name
  • Jun Ma — 132 papers, h 26
  • Jun Ma — 30 papers, h 11
  • Jun Ma — 26 papers, h 21
  • Jun Ma — 25 papers, h 10
  • Jun Ma — 19 papers
  • Jun Ma — 17 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedSegment Anything in Medical Images

2.8k citations · 3.1k across the 7 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

eess.IV2024★ 2 cited

Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

Jun Ma, Yao Zhang, Song Gu +7

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are…

eess.IV2024★ 11 cited

Segment Anything in Medical Images and Videos: Benchmark and Deployment

Jun Ma, Sumin Kim, Feifei Li +4

Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical da…

cs.CV2024★ 1 cited

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge

Hao Ding, Yuqian Zhang, Tuxun Lu +39

Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptibl…

eess.IV2024★ 230 cited

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Jun Ma, Feifei Li, Bo Wang

Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle lo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.