activity
20172024
most citedBoundary-aware Transformers for Skin Lesion Segmentation

136 citations · 230 across the 15 of their papers we have counts for

collaborators

19 papers

cs.CV2025

Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation

Xinyi Tan, Jiacheng Wang, Liansheng Wang

Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmen…

cs.CV2024

GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation

Runsheng Liu, Hao Jiang, Yanning Zhou +3

Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of di…

cs.CV202212 cited

Lesion Guided Explainable Few Weak-shot Medical Report Generation

Jinghan Sun, Dong Wei, Liansheng Wang +1

Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care…

eess.IV20225 cited

Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19

Liansheng Wang, Jiacheng Wang, Lei Zhu +6

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. However, there are still some challenges for developing AI sy…

eess.IV20228 cited

UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

Jiansen Guo, Hong-Yu Zhou, Liansheng Wang +1

Recent medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture. However, one potential drawb…

cs.CV20224 cited

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation

Cong Xie, Hualuo Liu, Shilei Cao +4

Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject si…