# Sen Yang

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/sen-yang/

## Facts

| Field | Value |
| --- | --- |
| Citations | 3,766 |
| Field | AI in cancer detection |
| h-index | 28 |
| i10-index | 48 |
| Last Known Institution | Central South University |
| OpenAlex ID | https://openalex.org/A5057464765 |
| ORCID iD | 0000-0002-0639-4122 |
| Works | 103 |

## Researcher papers

- [Transformer-based unsupervised contrastive learning for histopathological image classification](https://scholariq.org/papers/transformer-based-unsupervised-contrastive-learning-for-histopathological-image/)
- [A pathology foundation model for cancer diagnosis and prognosis prediction](https://scholariq.org/papers/a-pathology-foundation-model-for-cancer-diagnosis-and-prognosis-prediction/)
- [RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval](https://scholariq.org/papers/retccl-clustering-guided-contrastive-learning-for-whole-slide-image-retrieval/)
- [Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution](https://scholariq.org/papers/predicting-lymph-node-metastasis-using-histopathological-images-based-on/)
- [A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans](https://scholariq.org/papers/a-deep-learning-algorithm-for-automatic-detection-and-classification-of-acute/)
- [A vision–language foundation model for precision oncology](https://scholariq.org/papers/a-vision-language-foundation-model-for-precision-oncology/)
- [Mitosis domain generalization in histopathology images — The MIDOG challenge](https://scholariq.org/papers/mitosis-domain-generalization-in-histopathology-images-the-midog-challenge/)
- [TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification](https://scholariq.org/papers/transpath-transformer-based-self-supervised-learning-for-histopathological-image/)
- [PAIP 2019: Liver cancer segmentation challenge](https://scholariq.org/papers/paip-2019-liver-cancer-segmentation-challenge/)
- [Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge](https://scholariq.org/papers/automated-segmentation-of-normal-and-diseased-coronary-arteries-the-asoca/)
- [Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning](https://scholariq.org/papers/few-shot-crop-disease-recognition-using-sequence-weighted-ensemble-model/)
- [HMFN-FSL: Heterogeneous Metric Fusion Network-Based Few-Shot Learning for Crop Disease Recognition](https://scholariq.org/papers/hmfn-fsl-heterogeneous-metric-fusion-network-based-few-shot-learning-for-crop/)

## Researcher topics

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)
- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

## Researcher university

- [Central South University](https://scholariq.org/institutions/central-south-university/)

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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.