85 citations · 273 across the 108 of their papers we have counts for
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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…
Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
Hairong Shi, Songhao Han, Shaofei Huang +6
Tumor lesion segmentation on CT or MRI images plays a critical role in cancer diagnosis and treatment planning. Considering the inherent differences in tumor lesion segmentation da…
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…
Lesion-aware Dynamic Kernel for Polyp Segmentation
Ruifei Zhang, Peiwen Lai, Xiang Wan +4
Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis. However, it has always been a challenging task due to 1) the diverse shape,…
BCNet: Bronchus Classification via Structure Guided Representation Learning
Wenhao Huang, Haifan Gong, Huan Zhang +5
CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challenging because bronchial topology varies s…
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation
Xinkai Zhao, Chaowei Fang, De-Jun Fan +3
Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of…