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Jun Shi

4 papers hereh-index 17617 citations48 works total

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

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • eess.IV2
same name
  • Jun Shi — 11 papers, h 35
  • Jun Shi — 9 papers, h 12
  • Jun Shi — 8 papers, h 2
  • Jun Shi — 6 papers, h 6
  • Jun Shi — 5 papers
  • Jun Shi — 5 papers, h 5

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

most citedMulti-scale Efficient Graph-Transformer for Whole Slide Image Classification

2 citations · 2 across the 3 of their papers we have counts for

collaborators

4 papers

eess.IV2024

Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images

Tianxiang Huang, Jing Shi, Ge Jin +4

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants. However, due to…

cs.CV2023

Multi-Scale Prototypical Transformer for Whole Slide Image Classification

Saisai Ding, Jun Wang, Juncheng Li +1

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accu…

eess.IV2023

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images

Jian Wang, Liang Qiao, Shichong Zhou +6

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automaticCAD system, lesion detection is critical for th…

cs.CV2023★ 2 cited

Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification

Saisai Ding, Juncheng Li, Jun Wang +2

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness…

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