A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts

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.

Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping

no code implementations • 18 Apr 2023 • Hanyu Cai, Ni Ou, Junzheng Wang

The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual-bootstrapped LiDAR poses initialization modifications.

Motion Compensation Visual Odometry

Unsupervised Brain Tumor Segmentation with Image-based Prompts

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?

Brain Tumor Segmentation Lesion Segmentation +2

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

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.

Data Augmentation Lesion Segmentation +1