416 citations · 2.7k across the 106 of their papers we have counts for
13 papers · 1 filter
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
Gangming Zhao, Kongming Liang, Chengwei Pan +4
Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use st…
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
Shu Zhang, Zihao Li, Hong-Yu Zhou +2
The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. As a result, constructing…
CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation
Xinru Zhang, Chenghao Liu, Ni Ou +5
Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentati…
Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
Chenxin Li, Wenao Ma, Liyan Sun +4
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel b…
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Jie Lian, Jingyu Liu, Shu Zhang +4
Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and dise…
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
Jiafa He, Chengwei Pan, Can Yang +4
Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from d…
Adaptive noise imitation for image denoising
Huangxing Lin, Yihong Zhuang, Yue Huang +4
The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work…
The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
Jie Lian, Jingyu Liu, Yizhou Yu +9
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab. The challenge is divided into two rounds. In this paper, we present the results of 6 teams wh…
Contralaterally Enhanced Networks for Thoracic Disease Detection
Gangming Zhao, Chaowei Fang, Guanbin Li +2
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapp…
Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation
Yicheng Wu, Chengwei Pan, Shuqi Wang +3
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being ext…
ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
Jingyu Liu, Jie Lian, Yizhou Yu
Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Most existing works on chest X-rays focus on disease class…
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
Shen Wang, Kongming Liang, Chengwei Pan +5
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI). The automated midline…
Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
Chaowei Fang, Guanbin Li, Chengwei Pan +2
Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address…