Search Results for author:
Found 9 papers, 4 papers with code
CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer
Recent deep multi-view stereo (MVS) methods have widely incorporated transformers into cascade network for high-resolution depth estimation, achieving impressive results.
MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT
Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques in computed tomography (CT).
Dual Feature Augmentation Network for Generalized Zero-shot Learning
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.
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks
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.
Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network
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.
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
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
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis
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.
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
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.
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
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.
