88 citations · 108 across the 4 of their papers we have counts for
5 papers
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Fangyijie Wang, Guénolé Silvestre, Ziyang Wang +1
Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annot…
VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module
Ziyang Wang, Jian-Qing Zheng, Chao Ma +1
Image registration, a critical process in medical imaging, involves aligning different sets of medical imaging data into a single unified coordinate system. Deep learning networks,…
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
Ziyang Wang, Jian-Qing Zheng, Yichi Zhang +2
In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks. While the former excels in capt…
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
Ziyang Wang, Chao Ma
Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamb…
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
Chao Ma, Ziyang Wang
Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements. Notably, the convolutional neural ne…