1 code implementation • 14 Dec 2023 • Sicheng Wang, Hao Jiang, Lei Xiang
Recent deep multi-view stereo (MVS) methods have widely incorporated transformers into cascade network for high-resolution depth estimation, achieving impressive results.
no code implementations • 14 Nov 2023 • Tao Song, Ruizhi Hou, Lisong Dai, Lei Xiang
Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques in computed tomography (CT).
1 code implementation • 25 Sep 2023 • Lei Xiang, Yuan Zhou, Haoran Duan, Yang Long
To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features.
no code implementations • 14 Oct 2022 • Ludovic Sibille, Xinrui Zhan, Lei Xiang
Goal: The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images in the context of the AutoPET challenge.
1 code implementation • 12 Aug 2021 • Kai Xuan, Lei Xiang, Xiaoqian Huang, Lichi Zhang, Shu Liao, Dinggang Shen, Qian Wang
However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice.
1 code implementation • CVPR 2020 • Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang
To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.
Ranked #9 on Image Dehazing on Haze4k
no code implementations • 7 Jul 2019 • Dong Nie, Lei Xiang, Qian Wang, Dinggang Shen
To address this issue, we propose a simple but effective strategy, that is, we propose a dual-discriminator (dual-D) adversarial learning system, in which, a global-D is used to make an overall evaluation for the synthetic image, and a local-D is proposed to densely evaluate the local regions of the synthetic image.
no code implementations • 21 May 2019 • Xuhua Ren, Lichi Zhang, Sahar Ahmad, Dong Nie, Fan Yang, Lei Xiang, Qian Wang, Dinggang Shen
In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to.
no code implementations • 7 Sep 2017 • Lei Xiang, Qian Wang, Xiyao Jin, Dong Nie, Yu Qiao, Dinggang Shen
After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN.