Search Results for author: Tong Tong

Found 7 papers, 1 papers with code

Architecture Disentanglement for Deep Neural Networks

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.

AutoML Disentanglement

Stain Style Transfer using Transitive Adversarial Networks

no code implementations23 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.

Style Transfer

Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network

no code implementations26 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.

Image Classification

Enhancement Mask for Hippocampus Detection and Segmentation

no code implementations12 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.

Hippocampus

Image Super-Resolution Using Dense Skip Connections

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.

Image Super-Resolution Single Image Super Resolution

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

no code implementations29 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.

Cardiac Segmentation

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