Our Works

BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-decoder Architecture (MICCAI2020)

Tiange Xiang¹, Chaoyi Zhang¹, Dongnan Liu¹, Yang Song², Heng Huang^3,4, and Weidong Cai¹.

BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation (MICCAI2021)

Xinyi Wang^1,*, Tiange Xiang^1,*, Chaoyi Zhang¹, Yang Song², Dongnan Liu¹, Heng Huang^3,4, and Weidong Cai¹.
(* Equal first-author contributions)

Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond (MIA2022)

Tiange Xiang¹, Chaoyi Zhang¹, Xinyi Wang¹, Yang Song², Dongnan Liu¹, Heng Huang^3,4, and Weidong Cai¹.

Overview

BiO-Net. In this paper, we present a novel Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a recurrent manner without introducing any extra parameters. Our proposed bi-directional skip connections can be directly adopted into any encoder-decoder architecture to further enhance its capabilities in various task domains. We evaluated our method on various medical image analysis tasks and the results show that our BiO-Net significantly outperforms the vanilla U-Net as well as other state-of-the-art methods.

BiX-NAS. In this work, we study a multi-scale (abstracted as 'X') upgrade of a bi-directional skip connected network and then automatically discover an efficient architecture by a novel two-phase Neural Architecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method reduces the network computational cost by sifting out ineffective multi-scale features at different levels and iterations. We evaluate BiX-NAS on two segmentation tasks using three different medical image datasets, and the experimental results show that our BiX-NAS searched architecture (BiX-Net) achieves the state-of-the-art performance with significantly lower computational cost.

Highlights

BiO-Net

• Present a compact substitute of U-Net with paired forward and backward skip connections.
• Recursed to reuse the parameters during training and inference.
• State-of-the-art mIoU (0.704) and DICE (0.825) on MoNuSeg without introducing auxiliary parameters.

BiX-NAS

• Present a multi-scale upgrade of BiO-Net++ following the recurrent bi-directional paradigm.
• Design an effective two-phase Neural Architecture Search (NAS) strategy overcoming the searching deficiency, namely BiX-NAS.
• State-of-the-art mIoU and DICE on three medical image datasets with significantly lower computational cost (0.38M parameters, 28.00G MACs), and the searched BiX-Net will be avaialble in our codebase for comparison.

Qualitative Results

Video

BibTeX

If you find our data or project useful in your research, please cite:

@inproceedings{xiang2020bio,
    title={BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture},
    author={Xiang, Tiange and Zhang, Chaoyi and Liu, Dongnan and Song, Yang and Huang, Heng and Cai, Weidong},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
    pages={74--84},
    year={2020},
    organization={Springer}
}

@inproceedings{wang2021bix,
    title={BiX-NAS: Searching efficient bi-directional architecture for medical image segmentation},
    author={Wang, Xinyi and Xiang, Tiange and Zhang, Chaoyi and Song, Yang and Liu, Dongnan and Huang, Heng and Cai, Weidong},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={229--238},
    year={2021},
    organization={Springer}
}

@inproceedings{xiang2022bio,
    title={Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond},
    author={Xiang, Tiange and Zhang, Chaoyi and Wang, Xinyi and and Song, Yang and Liu, Dongnan and Huang, Heng and Cai, Weidong},
    booktitle={Medical Image Analysis},
    year={2022},
    organization={Springer}
}

Acknowledgments

The website template was borrowed from Chaoyi Zhang.