Search Results for author: Shichuan Zhang

Found 5 papers, 3 papers with code

Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images

no code implementations27 Feb 2022 Shichuan Zhang, Chenglu Zhu, Honglin Li, Jiatong Cai, Lin Yang

We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches.

Multi-Task Learning Representation Learning

Generalizing Nucleus Recognition Model in Multi-source Images via Pruning

no code implementations6 Jul 2021 Jiatong Cai, Chenglu Zhu, Can Cui, Honglin Li, Tong Wu, Shichuan Zhang, Lin Yang

In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains.

Domain Generalization

Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss

1 code implementation3 Feb 2021 Wenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang

Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.

Computed Tomography (CT)

Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

1 code implementation13 Dec 2020 Xiangde Luo, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, Nianyong Chen, Guotai Wang, Shaoting Zhang

In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation.

Cannot find the paper you are looking for? You can Submit a new open access paper.