# Junzhou Huang

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/junzhou-huang/

## Facts

| Field | Value |
| --- | --- |
| Citations | 25,720 |
| Field | AI in cancer detection |
| h-index | 77 |
| i10-index | 294 |
| Last Known Institution | The University of Texas at Arlington |
| OpenAlex ID | https://openalex.org/A5068865316 |
| ORCID iD | https://orcid.org/0000-0002-9548-1227 |
| Works | 542 |

## Researcher papers

- [Transformer-based unsupervised contrastive learning for histopathological image classification](https://scholariq.org/papers/transformer-based-unsupervised-contrastive-learning-for-histopathological-image/)
- [Early triage of critically ill COVID-19 patients using deep learning](https://scholariq.org/papers/early-triage-of-critically-ill-covid-19-patients-using-deep-learning/)
- [Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer](https://scholariq.org/papers/development-and-interpretation-of-a-pathomics-based-model-for-the-prediction-of/)
- [RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval](https://scholariq.org/papers/retccl-clustering-guided-contrastive-learning-for-whole-slide-image-retrieval/)

## Researcher topics

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)
- [Sparse and Compressive Sensing Techniques](https://scholariq.org/topics/sparse-and-compressive-sensing-techniques/)
- [Computational Drug Discovery Methods](https://scholariq.org/topics/computational-drug-discovery-methods/)
- [Domain Adaptation and Few-Shot Learning](https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/)

## Researcher university

- [The University of Texas at Arlington](https://scholariq.org/institutions/the-university-of-texas-at-arlington/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.