no code implementations • 16 May 2024 • Xinru Zhang, Ni Ou, Berke Doga Basaran, Marco Visentin, Mengyun Qiao, Renyang Gu, Cheng Ouyang, Yaou Liu, Paul M. Matthew, Chuyang Ye, Wenjia Bai
In this work, we propose a universal foundation model for 3D brain lesion segmentation, which can automatically segment different types of brain lesions for input data of various imaging modalities.
no code implementations • 2 Feb 2024 • Ruizhi Zhu, Xinru Zhang, Haowen Pang, Chundan Xu, Chuyang Ye
Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images.
no code implementations • 4 Apr 2023 • Xinru Zhang, Ni Ou, Chenghao Liu, Zhizheng Zhuo, Yaou Liu, Chuyang Ye
Specifically, instead of directly training a model for brain tumor segmentation with a large amount of annotated data, we seek to train a model that can answer the question: is a voxel in the input image associated with tumor-like hyper-/hypo-intensity?
no code implementations • 4 Feb 2023 • Weidong Ji, Zengxiang Yin, Guohui Zhou, Yuqi Yue, Xinru Zhang, Chenghong Sun
Translation models tend to ignore the rich semantic information in triads in the process of knowledge graph complementation.
1 code implementation • 16 Aug 2021 • Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye
Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.