1 code implementation • 20 Mar 2024 • Yizhu Wen, Kai Yi, Jing Ke, Yiqing Shen
Specifically, DiffImpute is trained on complete tabular datasets, ensuring that it can produce credible imputations for missing entries without undermining the authenticity of the existing data.
1 code implementation • 16 Jul 2023 • Zhenqi He, Mathias Unberath, Jing Ke, Yiqing Shen
In conclusion, TransNuSeg confirms the strength of Transformer in the context of nuclei segmentation, which thus can serve as an efficient solution for real clinical practice.
1 code implementation • 25 Jun 2022 • Yiqing Shen, Yulin Luo, Dinggang Shen, Jing Ke
To address the problems, we unify SN and SA with a novel RandStainNA scheme, which constrains variable stain styles in a practicable range to train a stain agnostic deep learning model.
no code implementations • 23 May 2018 • Zhuoran Song, Ru Wang, Dongyu Ru, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, Li Jiang
In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and predefined patterns to eliminate the unnecessary computation and data access.