1 code implementation • 1 May 2024 • Jun Shi, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Hong An, Xudong Xue, Bing Yan
Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images.
1 code implementation • 4 Feb 2024 • Liang Qiao, Jun Shi, Xiaoyu Hao, Xi Fang, Minfan Zhao, Ziqi Zhu, Junshi Chen, Hong An, Bing Li, Honghui Yuan, Xinyang Wang
Tensor program optimization on Deep Learning Accelerators (DLAs) is critical for efficient model deployment.
1 code implementation • 4 Jul 2023 • Jun Shi, Hongyu Kan, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Liang Qiao, Zhaohui Wang, Hong An, Xudong Xue
In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures.