BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification

Churan Wang, Fandong Zhang, Yizhou Yu, Yizhou Wang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020

Paper

• Propose a residual-preserved mechanism with CycleGAN framework based on the bilateral symmetry prior to generate healthy mammogram features for malignancy classification.

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

Churan Wang*, Jing Li*, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang

IEEE Transactions on Image Processing (TIP), 2021

Paper

• Introduce a counterfactual generative network leveraging the bilateral symmetry prior to enhance mammogram diagnosis performance.

Dae-gcn: Identifying Disease-related Features for Disease Prediction

Churan Wang, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021

Paper

• Learning disease-related representations in medical images by integrating a disentangling mechanism with graph convolutional network, which significantly enhances the performance and interpretability of cancer diagnosis.

Disentangling Disease-related Representation from Obscure for Disease Prediction

Churan Wang, Fei Gao, Fandong Zhang, Fangwei Zhong, Yizhou Yu, Yizhou Wang

International Conference on Machine Learning (ICML), 2022

Paper

• A disentanglement learning strategy under the guidance of alpha blending generation in an encoder-decoder framework (DAB-Net).

Prediction of Carotid In-Stent Restenosis by Computed Tomography Angiography Carotid Plaque-Based Radiomics

Xiaoqing Cheng, Zheng Dong, Jia Liu, Hongxia Li, Changsheng Zhou, Fandong Zhang, Churan Wang, Zhiqiang Zhang, Guangming Lu

Journal of Clinical Medicine (JCM), 2022

Paper

• Retrospectively analyze clinical imaging data to identify independent predictors of in-stent restenosis following carotid artery stenting, demonstrating that a combination of traditional plaque characteristics and radiomic features provides the highest predictive accuracy for post-procedure outcomes.

Learning Domain-agnostic Representation for Disease Diagnosis

Churan Wang, Jing Li, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang

The Eleventh International Conference on Learning Representations (ICLR), 2023

Paper

• Disentangle disease-related features from center-effects, enhancing robustness in multi-center image-based diagnosis.

Semi-and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations

Xinyu Xiong, Churan Wang✉, Wenxue Li, Guanbin Li✉

IEEE International Symposium on Biomedical Imaging (ISBI), 2024

Paper

• Present a semi- and weakly-supervised learning framework for breast mass segmentation that effectively addresses the challenge of identifying small, camouflaged masses in breast cancer diagnosis and the high cost of pixel-wise annotations.

Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation

Fei Gao, Siwen Wang, Fandong Zhang, Hong-Yu Zhou, Yizhou Wang, Churan Wang✉, Gang Yu✉, Yizhou Yu✉

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024

Paper

• Integrate 2D and 3D medical imaging data through a pseudo-3D transformation, enhancing the efficiency and effectiveness of SSL for 3D medical image analysis and demonstrating superior performance across various downstream tasks.

Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL

Fangwei Zhong, Kui Wu, Hai Ci, Churan Wang, Hao Chen

The 18th European Conference on Computer Vision (ECCV), 2024

Project, Paper

• Significantly enhanced the understanding and adaptive capabilities of systems in dynamic environments with visual foundation models and offline RL.