1 code implementation • ICCV 2021 • Jie Hu, Liujuan Cao, Qixiang Ye, Tong Tong, Shengchuan Zhang, Ke Li, Feiyue Huang, Rongrong Ji, Ling Shao
Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs.
1 code implementation • ICCV 2023 • Wenxuan Zeng, Meng Li, Wenjie Xiong, Tong Tong, Wen-jie Lu, Jin Tan, Runsheng Wang, Ru Huang
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
1 code implementation • 31 Aug 2023 • Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Shun Chen, Tao Tan, Xinlin Zhang, Tong Tong
In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation.
1 code implementation • 17 Nov 2023 • Tao Wang, Yuanbin Chen, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Bizhe Bai, Tao Tan, Min Du, Qinquan Gao, Tong Tong
Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
1 code implementation • 13 Dec 2023 • Yuanbo Zhou, Yuyang Xue, Jiang Bi, Wenlin He, Xinlin Zhang, Jiajun Zhang, Wei Deng, Ruofeng Nie, Junlin Lan, Qinquan Gao, Tong Tong
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems.
1 code implementation • 29 Jun 2023 • Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong
To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
no code implementations • 29 Apr 2016 • Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.
no code implementations • 3 Oct 2018 • Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X. Pham, Cao Van Nguyen, Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou, Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc van Gool, Etienne de Stoutz, Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
no code implementations • ICCV 2017 • Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao
In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network.
no code implementations • 12 Feb 2019 • Dengsheng Chen, Wenxi Liu, You Huang, Tong Tong, Yuanlong Yu
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
no code implementations • 26 Feb 2019 • Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, the Alzheimer's Disease Neuroimaging Initiative
In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.
no code implementations • 23 Oct 2019 • Shaojin Cai, Yuyang Xue3 Qinquan Gao, Min Du, Gang Chen, Hejun Zhang, Tong Tong
It is not necessary for an expert to pick a representative reference slide in the proposed TAN method.
no code implementations • 24 Nov 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Chan-Tong Lam, Tong Tong, Hao Chen, Qinquan Gao, Wei Ke, Tao Tan
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
1 code implementation • 29 Dec 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Wei Ke, Chan-Tong Lam, Jiangang Chen, Mingfeng Jiang, Mingwei Wang, Hui Xie, Tong Tong, Qinquan Gao, Hao Chen, Tao Tan
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
no code implementations • 23 Mar 2024 • Ruige Zong, Tao Wang, Chunwang Li, Xinlin Zhang, Yuanbin Chen, Longxuan Zhao, Qixuan Li, Qinquan Gao, Dezhi Kang, Fuxin Lin, Tong Tong
To alleviate this problem, we propose a quantitative statistical framework for FCCM, comprising an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module.