◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Yuankai Huo

28 papers hereh-index 697 citations49 works total

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

author position
  • middle author6
  • last author17

Across the 23 of 28 papers where every author was matched, so the position is known.

fields
  • cs.CV22
  • eess.IV4
  • cs.AI1
  • q-bio.QM1
same name
  • Yuankai Huo — 125 papers, h 39
  • Yuankai Huo — 23 papers, h 9
  • Yuankai Huo — 11 papers, h 4
  • Yuankai Huo — 1 paper
  • Yuankai Huo — 1 paper, h 1

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
20242026
most citedASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics

1 citations · 2 across the 24 of their papers we have counts for

collaborators
Showing eess.IVShow all

4 papers · 1 filter

eess.IV2025

IRS: Incremental Relationship-guided Segmentation for Digital Pathology

Ruining Deng, Junchao Zhu, Juming Xiong +14

Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practica…

eess.IV2025

PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit

Yuechen Yang, Yu Wang, Tianyuan Yao +5

Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. However, traditional feature extract…

eess.IV2024

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Jun Ma, Feifei Li, Sumin Kim +79

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…

eess.IV2024

Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology

Junlin Guo, Siqi Lu, Can Cui +14

Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datas…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.