# Feiwei Qin

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/feiwei-qin/

## Facts

| Field | Value |
| --- | --- |
| Citations | 2,350 |
| Field | 3D Shape Modeling and Analysis |
| h-index | 26 |
| i10-index | 38 |
| Last Known Institution | Hangzhou Dianzi University |
| OpenAlex ID | https://openalex.org/A5052124467 |
| ORCID iD | 0000-0001-5036-9365 |
| Works | 157 |

## Researcher papers

- [Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases](https://scholariq.org/papers/accurate-leukocyte-detection-based-on-deformable-detr-and-multi-level-feature/)
- [EEG classification of driver mental states by deep learning](https://scholariq.org/papers/eeg-classification-of-driver-mental-states-by-deep-learning/)
- [Fine-grained leukocyte classification with deep residual learning for microscopic images](https://scholariq.org/papers/fine-grained-leukocyte-classification-with-deep-residual-learning-for/)
- [GFIL: A Unified Framework for the Importance Analysis of Features, Frequency Bands, and Channels in EEG-Based Emotion Recognition](https://scholariq.org/papers/gfil-a-unified-framework-for-the-importance-analysis-of-features-frequency-bands/)
- [A miR-135b-TAZ positive feedback loop promotes epithelial–mesenchymal transition (EMT) and tumorigenesis in osteosarcoma](https://scholariq.org/papers/a-mir-135b-taz-positive-feedback-loop-promotes-epithelial-mesenchymal-transition/)
- [White Blood Cells Classification with Deep Convolutional Neural Networks](https://scholariq.org/papers/white-blood-cells-classification-with-deep-convolutional-neural-networks/)
- [A deep learning approach to the classification of 3D CAD models](https://scholariq.org/papers/a-deep-learning-approach-to-the-classification-of-3d-cad-models/)
- [Recurrent neural network from adder’s perspective: Carry-lookahead RNN](https://scholariq.org/papers/recurrent-neural-network-from-adder-s-perspective-carry-lookahead-rnn/)
- [FuS-GCN: Efficient B-rep based graph convolutional networks for 3D-CAD model classification and retrieval](https://scholariq.org/papers/fus-gcn-efficient-b-rep-based-graph-convolutional-networks-for-3d-cad-model/)
- [MsgFusion: Medical Semantic Guided Two-Branch Network for Multimodal Brain Image Fusion](https://scholariq.org/papers/msgfusion-medical-semantic-guided-two-branch-network-for-multimodal-brain-image/)
- [Healthcare resource utilization and caregiver burden associated with rotavirus gastroenteritis hospitalizations in Taiwan](https://scholariq.org/papers/healthcare-resource-utilization-and-caregiver-burden-associated-with-rotavirus/)

## Researcher topics

- [3D Shape Modeling and Analysis](https://scholariq.org/topics/3d-shape-modeling-and-analysis/)
- [Advanced Image Fusion Techniques](https://scholariq.org/topics/advanced-image-fusion-techniques/)
- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)
- [Manufacturing Process and Optimization](https://scholariq.org/topics/manufacturing-process-and-optimization/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

## Researcher university

- [Hangzhou Dianzi University](https://scholariq.org/institutions/hangzhou-dianzi-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.