5 papers · 1 filter
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
An Sui, Yuzhu Li, Gunter Schumann +2
Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation.…
ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
Ke Zhang, Bomin Wang, Hangqi Zhou +1
Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annota…
Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study
Bomin Wang, Hangqi Zhou, Yibo Gao +1
Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve…
Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss
Lin Xi, Yingliang Ma, Xiahai Zhuang
We introduce a novel FSVOS model that employs a local matching strategy to restrict the search space to the most relevant neighboring pixels. Rather than relying on inefficient sta…
Incorporating Pre-training Data Matters in Unsupervised Domain Adaptation
Yinsong Xu, Aidong Men, Yang Liu +2
In deep learning, initializing models with pre-trained weights has become the de facto practice for various downstream tasks. Many unsupervised domain adaptation (UDA) methods typi…