no code implementations • 6 Jul 2023 • Yuanjing Feng, Lei Xie, Jingqiang Wang, Jianzhong He, Fei Gao
At the global level, the tractography process is simplified as the estimation of bundle-specific tractogram distribution (BTD) coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors.
no code implementations • 9 Jun 2023 • Jianzhong He, Fan Zhang, Yiang Pan, Yuanjing Feng, Jarrett Rushmore, Erickson Torio, Yogesh Rathi, Nikos Makris, Ron Kikinis, Alexandra J. Golby, Lauren J. O'Donnell
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
no code implementations • 15 Nov 2022 • Sipei Li, Jianzhong He, Tengfei Xue, Guoqiang Xie, Shun Yao, Yuqian Chen, Erickson F. Torio, Yuanjing Feng, Dhiego CA Bastos, Yogesh Rathi, Nikos Makris, Ron Kikinis, Wenya Linda Bi, Alexandra J Golby, Lauren J O'Donnell, Fan Zhang
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus.
no code implementations • 19 Jul 2022 • Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu, Jinze Li, Jianzhong He, Tao Bai
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
no code implementations • 6 Jul 2022 • Yuqian Chen, Fan Zhang, Chaoyi Zhang, Tengfei Xue, Leo R. Zekelman, Jianzhong He, Yang song, Nikos Makris, Yogesh Rathi, Alexandra J. Golby, Weidong Cai, Lauren J. O'Donnell
In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).
no code implementations • 3 Jul 2022 • Jinze Li, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Shaoyun Xu, Tao Bai
The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.
no code implementations • CVPR 2023 • Shengsen Wu, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Ling-Yu Duan
Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.
no code implementations • CVPR 2022 • Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng
Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues.
Ranked #5 on Universal Domain Adaptation on Office-Home
1 code implementation • ICCV 2021 • Ruihuang Li, Xu Jia, Jianzhong He, Shuaijun Chen, QinGhua Hu
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.
Ranked #2 on Unsupervised Domain Adaptation on PACS
no code implementations • CVPR 2021 • Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian
Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.
no code implementations • CVPR 2021 • Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on Domain Adaptation on GTAV to Cityscapes+Mapillary
no code implementations • CVPR 2021 • Jianzhong He, Xu Jia, Shuaijun Chen, Jianzhuang Liu
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.
Ranked #1 on Domain Adaptation on GTA5+Synscapes to Cityscapes
Multi-Source Unsupervised Domain Adaptation Semantic Segmentation +1
1 code implementation • CVPR 2021 • Shuaijun Chen, Xu Jia, Jianzhong He, Yongjie Shi, Jianzhuang Liu
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
no code implementations • 17 Nov 2020 • Longhui Wei, Lingxi Xie, Jianzhong He, Jianlong Chang, Xiaopeng Zhang, Wengang Zhou, Houqiang Li, Qi Tian
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
no code implementations • 24 Jun 2020 • Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Qi Tian
This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.
2 code implementations • CVPR 2019 • Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Ranked #2 on Edge Detection on BRIND