Interactive medical image segmentation: A benchmark dataset and baseline
Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited
availability of large-scale, diverse, and densely annotated datasets, which hinders model …
availability of large-scale, diverse, and densely annotated datasets, which hinders model …
Enhancing semi-supervised medical image segmentation with bidirectional copy-paste and masked image reconstruction
To avoid the time-consuming and specialized task of medical image annotation, semi-
supervised medical image segmentation methods encourage models to leverage large …
supervised medical image segmentation methods encourage models to leverage large …
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
The limited availability of labeled data has driven advancements in semi-supervised
learning for medical image segmentation. Modern large-scale models tailored for general …
learning for medical image segmentation. Modern large-scale models tailored for general …
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
S Li, L Qi, Q Yu, J Huo, Y Shi… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical
image segmentation in a fully supervised manner, but requires precise annotations. To …
image segmentation in a fully supervised manner, but requires precise annotations. To …
Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation
H Liu, P Ren, Y Yuan, C Song… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
In semi-supervised medical image segmentation, the issue of fuzzy boundaries for
segmented objects arises. With limited labeled data and the interaction of boundaries from …
segmented objects arises. With limited labeled data and the interaction of boundaries from …
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) for medical image segmentation is a challenging yet highly
practical task, which reduces reliance on large-scale labeled dataset by leveraging …
practical task, which reduces reliance on large-scale labeled dataset by leveraging …
Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy
Y Wen, E Roussinova, O Brina, P Machi… - arXiv preprint arXiv …, 2024 - arxiv.org
Guidewire segmentation during endovascular interventions holds the potential to
significantly enhance procedural accuracy, improving visualization and providing critical …
significantly enhance procedural accuracy, improving visualization and providing critical …