Search Results for author: Shichuan Zhang

Found 17 papers, 7 papers with code

Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels

no code implementations24 Jun 2024 Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng

Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches.

Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis

no code implementations3 Jan 2024 Shichuan Zhang, Sunyi Zheng, Zhongyi Shui, Honglin Li, Lin Yang

Using multi-modal data, whole slide images (WSIs) and clinical information, can improve the performance of deep learning models in the diagnosis of axillary lymph node metastasis.

Decision Making whole slide images

Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency

no code implementations18 Sep 2023 Meng Han, Xiangde Luo, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang

Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with one encoder and three decoders using different dilation rates to capture features from different receptive fields that are complementary to each other to generate high-quality soft pseudo labels.

Computed Tomography (CT) Decoder +3

Masked conditional variational autoencoders for chromosome straightening

no code implementations25 Jun 2023 Jingxiong Li, Sunyi Zheng, Zhongyi Shui, Shichuan Zhang, Linyi Yang, Yuxuan Sun, Yunlong Zhang, Honglin Li, Yuanxin Ye, Peter M. A. van Ooijen, Kang Li, Lin Yang

This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results.

Semi-supervised Cell Recognition under Point Supervision

no code implementations14 Jun 2023 Zhongyi Shui, Yizhi Zhao, Sunyi Zheng, Yunlong Zhang, Honglin Li, Shichuan Zhang, Xiaoxuan Yu, Chenglu Zhu, Lin Yang

Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training.

whole slide images

DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection

no code implementations5 Mar 2023 Zhongyi Shui, Sunyi Zheng, Chenglu Zhu, Shichuan Zhang, Xiaoxuan Yu, Honglin Li, Jingxiong Li, Pingyi Chen, Lin Yang

Unlike mainstream PCD methods that rely on intermediate density map representations, the Point-to-Point network (P2PNet) has recently emerged as an end-to-end solution for PCD, demonstrating impressive cell detection accuracy and efficiency.

Cell Detection

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

1 code implementation22 Nov 2022 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.

Disentanglement Domain Generalization +4

Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images

no code implementations14 Oct 2022 Pingyi Chen, Chenglu Zhu, Zhongyi Shui, Jiatong Cai, Sunyi Zheng, Shichuan Zhang, Lin Yang

To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task.

Image Segmentation Medical Image Segmentation +3

End-to-end cell recognition by point annotation

no code implementations1 Jul 2022 Zhongyi Shui, Shichuan Zhang, Chenglu Zhu, BingChuan Wang, Pingyi Chen, Sunyi Zheng, Lin Yang

Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification.

Cell Detection Multi-Task Learning

HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy

1 code implementation7 Jun 2022 Hao Fu, Guotai Wang, Wenhui Lei, Wei Xu, Qianfei Zhao, Shichuan Zhang, Kang Li, Shaoting Zhang

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.

Segmentation

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 +1

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) Segmentation

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

2 code implementations13 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.

Segmentation

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