This survey extends beyond traditional and deep learning-based augmentation techniques or deep semi-supervised approaches, by explicitly focusing on medical/clinical imaging modalities, by explicitly focusing on CT, MRI, and X-ray, offering a broader perspective.
It is suggested that ten annotated cases are sufficient for clinically useful segmentation, effectively reducing bottlenecks for both image annotation and training time.
S. Nagaraju, B. Abrahamsen, Ashkan Moradi et al.· 0 citations
RegAL is proposed, a unified active semi-supervised framework governed by a shared topology-aware Pareto optimization that couples sample acquisition with unlabeled data utilization and consistently outperforms state-of-the-art AL, SSL, and active semi-supervised baselines across Dice and boundary-distance metrics unde...
Bahram Jafrasteh, Cheng Wan, Heejong Kim et al.· arXiv.org· 0 citations
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
Seyed Sina Ziaee, K. Ovens· Journal of Imaging· 1 citation
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Musa Doğan, I. Ozkan· BMC Medical Imaging· 0 citations
This paper proposes a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures and applies a novel consistency constraint by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an e...
Waqas Anwaar, Van Manh, Wufeng Xue et al.· Interdisciplinary Sciences C...· 0 citations
Fully convolutional encoder-decoder networks, in particular U-Net, are the leading method for dense pixel-level classification among the deep learning techniques and do not have any black-box problem and that makes the faster and more accurate in segmentation.
Pankaj Haribhua Chandankhede, Niraj K. Nagrale, Pragati Fatinge et al.· Journal of Advances in Devel...· 0 citations
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