8 citations · 19 across the 12 of their papers we have counts for
12 papers
Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models
Haifan Gong, Yitao Wang, Yihan Wang +3
The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges, particularly due to the domain gap between labeled datasets from well-r…
Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation
Haifan Gong, Wenhao Huang, Huan Zhang +5
Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bro…
Self-Supervised Alignment Learning for Medical Image Segmentation
Haofeng Li, Yiming Ouyang, Xiang Wan
Recently, self-supervised learning (SSL) methods have been used in pre-training the segmentation models for 2D and 3D medical images. Most of these methods are based on reconstruct…
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
Haifan Gong, Luoyao Kang, Yitao Wang +2
In the field of biomedical image analysis, the quest for architectures capable of effectively capturing long-range dependencies is paramount, especially when dealing with 3D image…
Cell Graph Transformer for Nuclei Classification
Wei Lou, Guanbin Li, Xiang Wan +1
Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze c…
UniCell: Universal Cell Nucleus Classification via Prompt Learning
Junjia Huang, Haofeng Li, Xiang Wan +1
The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but thei…
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Wei Lou, Xinyi Yu, Chenyu Liu +4
Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first deve…
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Junjia Huang, Haofeng Li, Weijun Sun +2
Automatic nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use o…
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
Zihang Xu, Haifan Gong, Xiang Wan +1
Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal bra…
Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction
Luoyao Kang, Haifan Gong, Xiang Wan +1
Deep learning (DL) has been used in the automatic diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) with brain imaging data. However, previous methods have…
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Junjia Huang, Haofeng Li, Xiang Wan +1
Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology. It is critical to efficiently locate and identify cells with diverse morphology…
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Xinyi Yu, Guanbin Li, Wei Lou +4
Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made sig…