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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…