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Huangshan Travel
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portfolio
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publications
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning IJCAI (oral) |
Chen, Yinda ; Huang, Wei ; Zhou, Shenglong ; Chen, Qi ; Xiong, Zhiwei
This paper proposes a decision - based MIM for neuron segmentation in EM data. It uses MARL to optimize masking, outperforming alternatives.
Citation (BibTeX):
@inproceedings{chen2023self,
title={Self-supervised neuron segmentation with multi-agent reinforcement learning},
author={Chen, Yinda and Huang, Wei and Zhou, Shenglong and Chen, Qi and Xiong, Zhiwei},
booktitle={Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence},
pages={609--617},
year={2023}
} Learning multiscale consistency for self-supervised electron microscopy instance segmentation ICASSP |
Chen, Yinda ; Huang, Wei ; Liu, Xiaoyu ; Deng, Shiyu ; Chen, Qi ; Xiong, Zhiwei
A pretraining framework for EM volume instance segmentation is proposed. It enforces multiscale consistency and shows good performance in neuron and mitochondria segmentation.
Citation (BibTeX):
@inproceedings{chen2024learning,
title={Learning multiscale consistency for self-supervised electron microscopy instance segmentation},
author={Chen, Yinda and Huang, Wei and Liu, Xiaoyu and Deng, Shiyu and Chen, Qi and Xiong, Zhiwei},
booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1566--1570},
year={2024},
organization={IEEE}
} Unsupervised Domain Adaptation for EM Image Denoising with Invertible Networks TMI |
Deng, Shiyu ; Chen, Yinda ; Huang, Wei ; Zhang, Ruobing ; Xiong, Zhiwei
The paper proposes an unsupervised domain adaptation method for EM image denoising with invertible networks, outperforming existing methods.
Citation (BibTeX):
@article{deng2024unsupervised,
title={Unsupervised Domain Adaptation for EM Image Denoising with Invertible Networks},
author={Deng, Shiyu and Chen, Yinda and Huang, Wei and Zhang, Ruobing and Xiong, Zhiwei},
journal={IEEE Transactions on Medical Imaging},
year={2024},
publisher={IEEE}
} BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval MICCAI |
Chen, Yinda ; Liu, Che ; Liu, Xiaoyu ; Arcucci, Rossella ; Xiong, Zhiwei
This paper presents BIMCV-R, a 3D CT text - image retrieval dataset, and MedFinder. Tests show MedFinder outperforms baselines in related tasks.
Citation (BibTeX):
@inproceedings{chen2024bimcv,
title={Bimcv-r: A landmark dataset for 3d ct text-image retrieval},
author={Chen, Yinda and Liu, Che and Liu, Xiaoyu and Arcucci, Rossella and Xiong, Zhiwei},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={124--134},
year={2024},
organization={Springer}
} MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation NeurIPS |
Qian, Haotian* ; Chen, Yinda* ; Lou, Shengtao ; Khan, Fahad Shahbaz ; Jin, Xiaogang ; Fan, Deng-Ping
MaskFactory proposes a two - stage method to generate high - quality synthetic datasets for DIS, outperforming existing methods in quality and efficiency.
Citation (BibTeX):
@article{qian2024mask,
title={MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation},
author={Qian, Haotian and Chen, YD and Lou, Shengtao and Khan, Fahad Shahbaz and Jin, Xiaogang and Fan, Deng-Ping},
journal={arXiv preprint arXiv:2412.19080},
year={2024}
} Condition-generation Latent Coding with an External Dictionary for Deep Image Compression AAAI (oral) |
Wu, Siqi* ; Chen, Yinda* ; Liu, Dong ; He, Zhihai
The paper proposes CLC for deep image compression. It uses a dictionary to generate references, shows good performance, and has theoretical analysis.
Citation (BibTeX):
@article{wu2023conditional,
title={Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression},
author={Wu, Siqi and Chen, Yinda and Liu, Dong and He, Zhihai},
journal={arXiv preprint arXiv:XXXX.XXXXX},
year={2025}
} talks
Talk on Medical Pretraining Using Generative Methods JD.com, Intelligent Healthcare Group |
Delivered a talk on the application of generative methods in vision-language models (VLMs) for medical pretraining. The presentation focused on leveraging advanced generative techniques to enhance performance in medical AI tasks.
teaching
XMU Wiserclub–Data Mining Xiamen University, WISER Club |
Responsible for designing and discussing the Data Mining course. Delivered lectures on Clustering and Feature Extraction topics.