Our group presents a novel approach to address the challenges in virtual staining of histopathological images. This work has been accepted and will be presented in AAAI2025 (CCF-A).

Congratulations to MixLab member Bing Xiong for honor the 2024 National Scholarship of UCAS!

The 3rd Computational Mathematics Modeling in Cancer Analysis(CMMCA2024) Successfully Held at MICCAI2024

This work introduces a novel unsupervised domain adaptation framework, DDSP, that addresses the instability issues in GAN-based medical image segmentation by making the model domain-agnostic and focusing on anatomical structure with semantic constraints, significantly outperforming existing methods on public datasets.

The miX-Lab's research findings in the field of pathological image generation have been accepted by the top conference on medical image analysis, MICCAI 2024.

The team proposed an unsupervised complex-valued network architecture based on physical information for efficient, high-quality color lens-free holographic reconstruction.

Congratulations Dr Tian for "Anatomy-Relation reasoning graph convolutional network for automatic fine-grained segmentation of organ's surgical anatomy" has been selected as cover article in IEEE Journal of Biomedical and Health Informatics.