Yinda Chen (陈胤达)

Yinda Chen (陈胤达)

he/him

PhD Candidate

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Unsupervised Domain Adaptation for EM Image Denoising with Invertible Networks TMI |

Deng, Shiyu ; Chen, Yinda ; Huang, Wei ; Zhang, Ruobing ; Xiong, Zhiwei

Paper Code
Main figure for Unsupervised Domain Adaptation for EM Image Denoising with Invertible Networks

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}
}

Conference Papers

Condition-generation Latent Coding with an External Dictionary for Deep Image Compression AAAI (oral) |

Wu, Siqi* ; Chen, Yinda* ; Liu, Dong ; He, Zhihai

Paper Code
Main figure for Condition-generation Latent Coding with an External Dictionary for Deep Image Compression

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}
}

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

Paper Code
Main figure for MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation

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}
}

BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval MICCAI |

Chen, Yinda ; Liu, Che ; Liu, Xiaoyu ; Arcucci, Rossella ; Xiong, Zhiwei

Paper Code
Main figure for BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval

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}
}

Learning multiscale consistency for self-supervised electron microscopy instance segmentation ICASSP |

Chen, Yinda ; Huang, Wei ; Liu, Xiaoyu ; Deng, Shiyu ; Chen, Qi ; Xiong, Zhiwei

Paper Code
Main figure for Learning multiscale consistency for self-supervised electron microscopy instance segmentation

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}
}

Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning IJCAI (oral) |

Chen, Yinda ; Huang, Wei ; Zhou, Shenglong ; Chen, Qi ; Xiong, Zhiwei

Paper Code
Main figure for Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning

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}
  }