A Distribu-tion-Constrained Semi-Supervised Segmentation Model (DCSSM) is introduced to capture positional correlations in medical images and achieves the best performance across multiple segmentation models on 5 medical segmentation datasets.
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
This study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data that enables precise tumor delineation and is suitable for large-scale clinical integration.
J. Adlin, Arockia Selva Saroja· ITEGAM- Journal of Engineeri...· 0 citations
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
Medical image segmentation plays an important role in clinical imaging for disease diagnosis, treatment planning, and follow-up analysis. Zero-shot segmentation models, like segment anything model are able to perform well but ultrasound images remain challenging due to speckle noise, low contrast and poor anatomical bo...
Trishita Acharjee, R. Naidu, Sangeetha.V· 2026 4th International Confe...· 0 citations
SimBIS improves biomedical image segmentation by incorporating Hu-moment-based global shape consistency into end-to-end optimization while retaining pixel-wise supervision.
Rongjia Lin, Zhidong Yang, Zi-Heng Xu et al.· BMC Medical Imaging· 0 citations
This work proposes a conditional Flow Matching (CFM) based framework for efficient 3D medical image segmentation that maintains competitive segmentation accuracy while achieving an inference time of only 1.14 seconds per volume, offering a clear efficiency advantage over existing generative segmentation methods.
Y. Yilihamu, Jian Xue, Chuang Jia et al.· International Conference on...· 0 citations
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