no code implementations • 25 Aug 2018 • Yueyue Wang, Liang Zhao, Zhijian Song, Manning Wang
Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer.
no code implementations • ECCV 2018 • Yinlong Liu, Chen Wang, Zhijian Song, Manning Wang
Three-dimensional rigid point cloud registration has many applications in computer vision and robotics.
no code implementations • 29 Dec 2018 • Xuechen Li, Yinlong Liu, Yiru Wang, Chen Wang, Manning Wang, Zhijian Song
However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient.
no code implementations • 25 Mar 2019 • Yinlong Liu, Xuechen Li, Manning Wang, Guang Chen, Zhijian Song, Alois Knoll
In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.
1 code implementation • 28 Jul 2020 • Shaolei Liu, Manning Wang, Zhijian Song
We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning.
2 code implementations • CVPR 2021 • Kexue Fu, Shaolei Liu, Xiaoyuan Luo, Manning Wang
In this paper, we propose a novel deep graph matchingbased framework for point cloud registration.
no code implementations • 12 Apr 2021 • Xiaoyuan Luo, Shaolei Liu, Kexue Fu, Manning Wang, Zhijian Song
In the UDA architecture, an encoder is shared between the networks for the self-supervised task and the main task of point cloud classification or segmentation, so that the encoder can be trained to extract features suitable for both the source and the target domain data.
5 code implementations • 12 May 2021 • Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, Manning Wang
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
Ranked #3 on Medical Image Segmentation on ACDC
2 code implementations • 2 Dec 2021 • Linhao Qu, Shaolei Liu, Manning Wang, Zhijian Song
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning.
2 code implementations • 22 Dec 2021 • Liang Pan, Tong Wu, Zhongang Cai, Ziwei Liu, Xumin Yu, Yongming Rao, Jiwen Lu, Jie zhou, Mingye Xu, Xiaoyuan Luo, Kexue Fu, Peng Gao, Manning Wang, Yali Wang, Yu Qiao, Junsheng Zhou, Xin Wen, Peng Xiang, Yu-Shen Liu, Zhizhong Han, Yuanjie Yan, Junyi An, Lifa Zhu, Changwei Lin, Dongrui Liu, Xin Li, Francisco Gómez-Fernández, Qinlong Wang, Yang Yang
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
no code implementations • 19 Jan 2022 • Linhao Qu, Shaolei Liu, Manning Wang, Shiman Li, Siqi Yin, Qin Qiao, Zhijian Song
In order to encourage different fusion tasks to promote each other and increase the generalizability of the trained network, we integrate the three self-supervised auxiliary tasks by randomly choosing one of them to destroy a natural image in model training.
no code implementations • 9 Feb 2022 • Kexue Fu, Peng Gao, Renrui Zhang, Hongsheng Li, Yu Qiao, Manning Wang
Especially, we develop a variant of ViT for 3D point cloud feature extraction, which also achieves comparable results with existing backbones when combined with our framework, and visualization of the attention maps show that our model does understand the point cloud by combining the global shape information and multiple local structural information, which is consistent with the inspiration of our representation learning method.
1 code implementation • 3 Apr 2022 • Kexue Fu, Peng Gao, Shaolei Liu, Renrui Zhang, Yu Qiao, Manning Wang
We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process.
1 code implementation • 17 Jun 2022 • Linhao Qu, Xiaoyuan Luo, Shaolei Liu, Manning Wang, Zhijian Song
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs).
1 code implementation • 27 Jul 2022 • Kexue Fu, Mingzhi Yuan, Manning Wang
Masked language modeling (MLM) has become one of the most successful self-supervised pre-training task.
no code implementations • 18 Aug 2022 • Linhao Qu, Siyu Liu, Xiaoyu Liu, Manning Wang, Zhijian Song
Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome.
1 code implementation • 1 Sep 2022 • Mingzhi Yuan, Zhihao LI, Qiuye Jin, Xinrong Chen, Manning Wang
Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud.
1 code implementation • 7 Oct 2022 • Linhao Qu, Xiaoyuan Luo, Manning Wang, Zhijian Song
Specifically, an attention-based bag classifier is used as the teacher network, which is trained with weak bag labels, and an instance classifier is used as the student network, which is trained using the normalized attention scores obtained from the teacher network as soft pseudo labels for the instances in positive bags.
1 code implementation • 9 Nov 2022 • Kexue Fu, Jiazheng Luo, Xiaoyuan Luo, Shaolei Liu, Chenxi Zhang, Manning Wang
In this paper, we propose a novel deep graph matching-based framework for point cloud registration.
no code implementations • 28 Nov 2022 • Shaolei Liu, Siqi Yin, Linhao Qu, Manning Wang
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain.
no code implementations • ICCV 2023 • Linhao Qu, Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song
However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.
no code implementations • 9 Apr 2023 • Linhao Qu, Minghong Duan, Zhiwei Yang, Manning Wang, Zhijian Song
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance.
1 code implementation • NeurIPS 2023 • Linhao Qu, Xiaoyuan Luo, Kexue Fu, Manning Wang, Zhijian Song
Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts.
no code implementations • 5 Jul 2023 • Linhao Qu, Yingfan Ma, Xiaoyuan Luo, Manning Wang, Zhijian Song
In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks.
1 code implementation • 11 Jul 2023 • Linhao Qu, Yingfan Ma, Zhiwei Yang, Manning Wang, Zhijian Song
In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.
1 code implementation • ICCV 2023 • Mingzhi Yuan, Kexue Fu, Zhihao LI, Yucong Meng, Manning Wang
Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications.
1 code implementation • 17 Oct 2023 • Shuo Wang, Yan Zhu, Xiaoyuan Luo, Zhiwei Yang, Yizhe Zhang, Peiyao Fu, Manning Wang, Zhijian Song, QuanLin Li, Pinghong Zhou, Yike Guo
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
1 code implementation • 22 Oct 2023 • Haoran Wang, Qiuye Jin, Shiman Li, Siyu Liu, Manning Wang, Zhijian Song
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets.
no code implementations • 28 Jan 2024 • Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song
To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in pathology images.